WorldWideScience

Sample records for spatial inference bacterial

  1. Spatial Inference Based on Geometric Proportional Analogies

    OpenAIRE

    Mullally, Emma-Claire; O'Donoghue, Diarmuid P.

    2006-01-01

    We describe an instance-based reasoning solution to a variety of spatial reasoning problems. The solution centers on identifying an isomorphic mapping between labelled graphs that represent some problem data and a known solution instance. We describe a number of spatial reasoning problems that are solved by generating non-deductive inferences, integrating topology with area (and other) features. We report the accuracy of our algorithm on different categories of spatial reasoning tasks from th...

  2. Forward and backward inference in spatial cognition.

    Directory of Open Access Journals (Sweden)

    Will D Penny

    Full Text Available This paper shows that the various computations underlying spatial cognition can be implemented using statistical inference in a single probabilistic model. Inference is implemented using a common set of 'lower-level' computations involving forward and backward inference over time. For example, to estimate where you are in a known environment, forward inference is used to optimally combine location estimates from path integration with those from sensory input. To decide which way to turn to reach a goal, forward inference is used to compute the likelihood of reaching that goal under each option. To work out which environment you are in, forward inference is used to compute the likelihood of sensory observations under the different hypotheses. For reaching sensory goals that require a chaining together of decisions, forward inference can be used to compute a state trajectory that will lead to that goal, and backward inference to refine the route and estimate control signals that produce the required trajectory. We propose that these computations are reflected in recent findings of pattern replay in the mammalian brain. Specifically, that theta sequences reflect decision making, theta flickering reflects model selection, and remote replay reflects route and motor planning. We also propose a mapping of the above computational processes onto lateral and medial entorhinal cortex and hippocampus.

  3. Models and Inference for Multivariate Spatial Extremes

    KAUST Repository

    Vettori, Sabrina

    2017-12-07

    The development of flexible and interpretable statistical methods is necessary in order to provide appropriate risk assessment measures for extreme events and natural disasters. In this thesis, we address this challenge by contributing to the developing research field of Extreme-Value Theory. We initially study the performance of existing parametric and non-parametric estimators of extremal dependence for multivariate maxima. As the dimensionality increases, non-parametric estimators are more flexible than parametric methods but present some loss in efficiency that we quantify under various scenarios. We introduce a statistical tool which imposes the required shape constraints on non-parametric estimators in high dimensions, significantly improving their performance. Furthermore, by embedding the tree-based max-stable nested logistic distribution in the Bayesian framework, we develop a statistical algorithm that identifies the most likely tree structures representing the data\\'s extremal dependence using the reversible jump Monte Carlo Markov Chain method. A mixture of these trees is then used for uncertainty assessment in prediction through Bayesian model averaging. The computational complexity of full likelihood inference is significantly decreased by deriving a recursive formula for the nested logistic model likelihood. The algorithm performance is verified through simulation experiments which also compare different likelihood procedures. Finally, we extend the nested logistic representation to the spatial framework in order to jointly model multivariate variables collected across a spatial region. This situation emerges often in environmental applications but is not often considered in the current literature. Simulation experiments show that the new class of multivariate max-stable processes is able to detect both the cross and inner spatial dependence of a number of extreme variables at a relatively low computational cost, thanks to its Bayesian hierarchical

  4. Dynamic spatial panels : models, methods, and inferences

    NARCIS (Netherlands)

    Elhorst, J. Paul

    This paper provides a survey of the existing literature on the specification and estimation of dynamic spatial panel data models, a collection of models for spatial panels extended to include one or more of the following variables and/or error terms: a dependent variable lagged in time, a dependent

  5. Data-driven inference for the spatial scan statistic

    Directory of Open Access Journals (Sweden)

    Duczmal Luiz H

    2011-08-01

    Full Text Available Abstract Background Kulldorff's spatial scan statistic for aggregated area maps searches for clusters of cases without specifying their size (number of areas or geographic location in advance. Their statistical significance is tested while adjusting for the multiple testing inherent in such a procedure. However, as is shown in this work, this adjustment is not done in an even manner for all possible cluster sizes. Results A modification is proposed to the usual inference test of the spatial scan statistic, incorporating additional information about the size of the most likely cluster found. A new interpretation of the results of the spatial scan statistic is done, posing a modified inference question: what is the probability that the null hypothesis is rejected for the original observed cases map with a most likely cluster of size k, taking into account only those most likely clusters of size k found under null hypothesis for comparison? This question is especially important when the p-value computed by the usual inference process is near the alpha significance level, regarding the correctness of the decision based in this inference. Conclusions A practical procedure is provided to make more accurate inferences about the most likely cluster found by the spatial scan statistic.

  6. Data-driven inference for the spatial scan statistic.

    Science.gov (United States)

    Almeida, Alexandre C L; Duarte, Anderson R; Duczmal, Luiz H; Oliveira, Fernando L P; Takahashi, Ricardo H C

    2011-08-02

    Kulldorff's spatial scan statistic for aggregated area maps searches for clusters of cases without specifying their size (number of areas) or geographic location in advance. Their statistical significance is tested while adjusting for the multiple testing inherent in such a procedure. However, as is shown in this work, this adjustment is not done in an even manner for all possible cluster sizes. A modification is proposed to the usual inference test of the spatial scan statistic, incorporating additional information about the size of the most likely cluster found. A new interpretation of the results of the spatial scan statistic is done, posing a modified inference question: what is the probability that the null hypothesis is rejected for the original observed cases map with a most likely cluster of size k, taking into account only those most likely clusters of size k found under null hypothesis for comparison? This question is especially important when the p-value computed by the usual inference process is near the alpha significance level, regarding the correctness of the decision based in this inference. A practical procedure is provided to make more accurate inferences about the most likely cluster found by the spatial scan statistic.

  7. Inference

    DEFF Research Database (Denmark)

    Møller, Jesper

    2010-01-01

    Chapter 9: This contribution concerns statistical inference for parametric models used in stochastic geometry and based on quick and simple simulation free procedures as well as more comprehensive methods based on a maximum likelihood or Bayesian approach combined with markov chain Monte Carlo...... (MCMC) techniques. Due to space limitations the focus is on spatial point processes....

  8. Bayesian Inference of Ecological Interactions from Spatial Data

    Directory of Open Access Journals (Sweden)

    Christopher R. Stephens

    2017-11-01

    Full Text Available The characterization and quantification of ecological interactions and the construction of species’ distributions and their associated ecological niches are of fundamental theoretical and practical importance. In this paper, we discuss a Bayesian inference framework, which, using spatial data, offers a general formalism within which ecological interactions may be characterized and quantified. Interactions are identified through deviations of the spatial distribution of co-occurrences of spatial variables relative to a benchmark for the non-interacting system and based on a statistical ensemble of spatial cells. The formalism allows for the integration of both biotic and abiotic factors of arbitrary resolution. We concentrate on the conceptual and mathematical underpinnings of the formalism, showing how, using the naive Bayes approximation, it can be used to not only compare and contrast the relative contribution from each variable, but also to construct species’ distributions and ecological niches based on an arbitrary variable type. We also show how non-linear interactions between distinct niche variables can be identified and the degree of confounding between variables accounted for.

  9. Spatial variation of bacterial community composition near the Luzon ...

    African Journals Online (AJOL)

    Spatial variation of bacterial community composition near the Luzon strait assessed by polymerase chain reaction-denaturing gradient gel electrophoresis ... chain reaction (PCR)-amplified bacterial 16S ribosomal deoxyribonucleic acid (DNA) gene fragments and interpreted the results; its relationship with physical and ...

  10. Interspecific bacterial interactions are reflected in multispecies biofilm spatial organization

    DEFF Research Database (Denmark)

    Liu, Wenzheng; Røder, Henriette Lyng; Madsen, Jonas Stenløkke

    2016-01-01

    not only the enabling sub-populations. However, the specific molecular mechanisms of cellular processes affecting spatial organization, and vice versa, are poorly understood and very complex to unravel. Therefore, detailed description of the spatial organization of individual bacterial cells...... environments. Species residing in these complex bacterial communities usually interact both intra- and interspecifically. Such interactions are considered to not only be fundamental in shaping overall biomass and the spatial distribution of cells residing in multispecies biofilms, but also to result......, industrial, and clinical implications. This review briefly presents the state of the art of studying interspecies interactions and spatial organization of multispecies communities, aiming to support theoretical and practical arguments for further advancement of this field....

  11. Spatial variation of bacterial community composition near the Luzon ...

    African Journals Online (AJOL)

    use

    2011-11-23

    Nov 23, 2011 ... However, little information of spatial variation of bacterial community ... GF/F glass fiber (0.45 µm, 47 mm diameter, Whatman Japan. Limited., Tokyo ... the research vessel using a CTD system (Sea-Bird Electronics, Inc.,. USA).

  12. Streptococcus massiliensis in the human mouth: a phylogenetic approach for the inference of bacterial habitats.

    Science.gov (United States)

    Póntigo, F; Silva, C; Moraga, M; Flores, S V

    2015-12-29

    Streptococcus is a diverse bacterial lineage. Species of this genus occupy a myriad of environments inside humans and other animals. Despite the elucidation of several of these habitats, many remain to be identified. Here, we explore a methodological approach to reveal unknown bacterial environments. Specifically, we inferred the phylogeny of the Mitis group by analyzing the sequences of eight genes. In addition, information regarding habitat use of species belonging to this group was obtained from the scientific literature. The oral cavity emerged as a potential, previously unknown, environment of Streptococcus massiliensis. This phylogeny-based prediction was confirmed by species-specific polymerase chain reaction (PCR) amplification. We propose employing a similar approach, i.e., use of bibliographic data and molecular phylogenetics as predictive methods, and species-specific PCR as confirmation, in order to reveal other unknown habitats in further bacterial taxa.

  13. Interspecific bacterial interactions are reflected in multispecies biofilm spatial organization

    Directory of Open Access Journals (Sweden)

    Wenzheng Liu

    2016-08-01

    Full Text Available Interspecies interactions are essential for the persistence and development of any kind of complex community, and microbial biofilms are no exception. Multispecies biofilms are structured and spatially defined communities that have received much attention due to their omnipresence in natural environments. Species residing in these complex bacterial communities usually interact both intra- and interspecifically. Such interactions are considered to not only be fundamental in shaping overall biomass and the spatial distribution of cells residing in multispecies biofilms, but also to result in coordinated regulation of gene expression in the different species present. These communal interactions often lead to emergent properties in biofilms, such as enhanced tolerance against antibiotics, host immune responses and other stresses, which have been shown to provide benefits to all biofilm members not only the enabling sub-populations. However, the specific molecular mechanisms of cellular processes affecting spatial organization, and vice versa, are poorly understood and very complex to unravel. Therefore, detailed description of the spatial organization of individual bacterial cells in multispecies communities can be an alternative strategy to reveal the nature of interspecies interactions of constituent species. Closing the gap between visual observation and biological processes may become crucial for resolving biofilm related problems, which is of utmost importance to environmental, industrial, and clinical implications. This review briefly presents the state of the art of studying interspecies interactions and spatial organization of multispecies communities, aiming to support theoretical and practical arguments for further advancement of this field.

  14. Approximate inference for spatial functional data on massively parallel processors

    DEFF Research Database (Denmark)

    Raket, Lars Lau; Markussen, Bo

    2014-01-01

    With continually increasing data sizes, the relevance of the big n problem of classical likelihood approaches is greater than ever. The functional mixed-effects model is a well established class of models for analyzing functional data. Spatial functional data in a mixed-effects setting...... in linear time. An extremely efficient GPU implementation is presented, and the proposed methods are illustrated by conducting a classical statistical analysis of 2D chromatography data consisting of more than 140 million spatially correlated observation points....

  15. Probabilistic and spatially variable niches inferred from demography

    Science.gov (United States)

    Jeffrey M. Diez; Itamar Giladi; Robert Warren; H. Ronald. Pulliam

    2014-01-01

    Summary 1. Mismatches between species distributions and habitat suitability are predicted by niche theory and have important implications for forecasting how species may respond to environmental changes. Quantifying these mismatches is challenging, however, due to the high dimensionality of species niches and the large spatial and temporal variability in population...

  16. Comparison of Urban Human Movements Inferring from Multi-Source Spatial-Temporal Data

    Science.gov (United States)

    Cao, Rui; Tu, Wei; Cao, Jinzhou; Li, Qingquan

    2016-06-01

    The quantification of human movements is very hard because of the sparsity of traditional data and the labour intensive of the data collecting process. Recently, much spatial-temporal data give us an opportunity to observe human movement. This research investigates the relationship of city-wide human movements inferring from two types of spatial-temporal data at traffic analysis zone (TAZ) level. The first type of human movement is inferred from long-time smart card transaction data recording the boarding actions. The second type of human movement is extracted from citywide time sequenced mobile phone data with 30 minutes interval. Travel volume, travel distance and travel time are used to measure aggregated human movements in the city. To further examine the relationship between the two types of inferred movements, the linear correlation analysis is conducted on the hourly travel volume. The obtained results show that human movements inferred from smart card data and mobile phone data have a correlation of 0.635. However, there are still some non-ignorable differences in some special areas. This research not only reveals the citywide spatial-temporal human dynamic but also benefits the understanding of the reliability of the inference of human movements with big spatial-temporal data.

  17. COMPARISON OF URBAN HUMAN MOVEMENTS INFERRING FROM MULTI-SOURCE SPATIAL-TEMPORAL DATA

    Directory of Open Access Journals (Sweden)

    R. Cao

    2016-06-01

    Full Text Available The quantification of human movements is very hard because of the sparsity of traditional data and the labour intensive of the data collecting process. Recently, much spatial-temporal data give us an opportunity to observe human movement. This research investigates the relationship of city-wide human movements inferring from two types of spatial-temporal data at traffic analysis zone (TAZ level. The first type of human movement is inferred from long-time smart card transaction data recording the boarding actions. The second type of human movement is extracted from citywide time sequenced mobile phone data with 30 minutes interval. Travel volume, travel distance and travel time are used to measure aggregated human movements in the city. To further examine the relationship between the two types of inferred movements, the linear correlation analysis is conducted on the hourly travel volume. The obtained results show that human movements inferred from smart card data and mobile phone data have a correlation of 0.635. However, there are still some non-ignorable differences in some special areas. This research not only reveals the citywide spatial-temporal human dynamic but also benefits the understanding of the reliability of the inference of human movements with big spatial-temporal data.

  18. Augmentation of Explicit Spatial Configurations by Knowledge-Based Inference on Geometric Fields

    Directory of Open Access Journals (Sweden)

    Dan Tappan

    2009-04-01

    Full Text Available A spatial configuration of a rudimentary, static, realworld scene with known objects (animals and properties (positions and orientations contains a wealth of syntactic and semantic spatial information that can contribute to a computational understanding far beyond what its quantitative details alone convey. This work presents an approach that (1 quantitatively represents what a configuration explicitly states, (2 integrates this information with implicit, commonsense background knowledge of its objects and properties, (3 infers additional, contextually appropriate, commonsense spatial information from and about their interrelationships, and (4 augments the original representation with this combined information. A semantic network represents explicit, quantitative information in a configuration. An inheritance-based knowledge base of relevant concepts supplies implicit, qualitative background knowledge to support semantic interpretation. Together, these structures provide a simple, nondeductive, constraint-based, geometric logical formalism to infer substantial implicit knowledge for intrinsic and deictic frames of spatial reference.

  19. Conditional predictive inference for online surveillance of spatial disease incidence

    Science.gov (United States)

    Corberán-Vallet, Ana; Lawson, Andrew B.

    2012-01-01

    This paper deals with the development of statistical methodology for timely detection of incident disease clusters in space and time. The increasing availability of data on both the time and the location of events enables the construction of multivariate surveillance techniques, which may enhance the ability to detect localized clusters of disease relative to the surveillance of the overall count of disease cases across the entire study region. We introduce the surveillance conditional predictive ordinate as a general Bayesian model-based surveillance technique that allows us to detect small areas of increased disease incidence when spatial data are available. To address the problem of multiple comparisons, we incorporate a common probability that each small area signals an alarm when no change in the risk pattern of disease takes place into the analysis. We investigate the performance of the proposed surveillance technique within the framework of Bayesian hierarchical Poisson models using a simulation study. Finally, we present a case study of salmonellosis in South Carolina. PMID:21898522

  20. Statistical inference and visualization in scale-space for spatially dependent images

    KAUST Repository

    Vaughan, Amy

    2012-03-01

    SiZer (SIgnificant ZERo crossing of the derivatives) is a graphical scale-space visualization tool that allows for statistical inferences. In this paper we develop a spatial SiZer for finding significant features and conducting goodness-of-fit tests for spatially dependent images. The spatial SiZer utilizes a family of kernel estimates of the image and provides not only exploratory data analysis but also statistical inference with spatial correlation taken into account. It is also capable of comparing the observed image with a specific null model being tested by adjusting the statistical inference using an assumed covariance structure. Pixel locations having statistically significant differences between the image and a given null model are highlighted by arrows. The spatial SiZer is compared with the existing independent SiZer via the analysis of simulated data with and without signal on both planar and spherical domains. We apply the spatial SiZer method to the decadal temperature change over some regions of the Earth. © 2011 The Korean Statistical Society.

  1. Statistical inference and visualization in scale-space for spatially dependent images

    KAUST Repository

    Vaughan, Amy; Jun, Mikyoung; Park, Cheolwoo

    2012-01-01

    SiZer (SIgnificant ZERo crossing of the derivatives) is a graphical scale-space visualization tool that allows for statistical inferences. In this paper we develop a spatial SiZer for finding significant features and conducting goodness-of-fit tests

  2. Bacterial phylogenetic reconstruction from whole genomes is robust to recombination but demographic inference is not.

    Science.gov (United States)

    Hedge, Jessica; Wilson, Daniel J

    2014-11-25

    Phylogenetic inference in bacterial genomics is fundamental to understanding problems such as population history, antimicrobial resistance, and transmission dynamics. The field has been plagued by an apparent state of contradiction since the distorting effects of recombination on phylogeny were discovered more than a decade ago. Researchers persist with detailed phylogenetic analyses while simultaneously acknowledging that recombination seriously misleads inference of population dynamics and selection. Here we resolve this paradox by showing that phylogenetic tree topologies based on whole genomes robustly reconstruct the clonal frame topology but that branch lengths are badly skewed. Surprisingly, removing recombining sites can exacerbate branch length distortion caused by recombination. Phylogenetic tree reconstruction is a popular approach for understanding the relatedness of bacteria in a population from differences in their genome sequences. However, bacteria frequently exchange regions of their genomes by a process called homologous recombination, which violates a fundamental assumption of phylogenetic methods. Since many researchers continue to use phylogenetics for recombining bacteria, it is important to understand how recombination affects the conclusions drawn from these analyses. We find that whole-genome sequences afford great accuracy in reconstructing evolutionary relationships despite concerns surrounding the presence of recombination, but the branch lengths of the phylogenetic tree are indeed badly distorted. Surprisingly, methods to reduce the impact of recombination on branch lengths can exacerbate the problem. Copyright © 2014 Hedge and Wilson.

  3. Inference

    DEFF Research Database (Denmark)

    Møller, Jesper

    (This text written by Jesper Møller, Aalborg University, is submitted for the collection ‘Stochastic Geometry: Highlights, Interactions and New Perspectives', edited by Wilfrid S. Kendall and Ilya Molchanov, to be published by ClarendonPress, Oxford, and planned to appear as Section 4.1 with the ......(This text written by Jesper Møller, Aalborg University, is submitted for the collection ‘Stochastic Geometry: Highlights, Interactions and New Perspectives', edited by Wilfrid S. Kendall and Ilya Molchanov, to be published by ClarendonPress, Oxford, and planned to appear as Section 4.......1 with the title ‘Inference'.) This contribution concerns statistical inference for parametric models used in stochastic geometry and based on quick and simple simulation free procedures as well as more comprehensive methods using Markov chain Monte Carlo (MCMC) simulations. Due to space limitations the focus...

  4. Phylogeny Inference of Closely Related Bacterial Genomes: Combining the Features of Both Overlapping Genes and Collinear Genomic Regions

    Science.gov (United States)

    Zhang, Yan-Cong; Lin, Kui

    2015-01-01

    Overlapping genes (OGs) represent one type of widespread genomic feature in bacterial genomes and have been used as rare genomic markers in phylogeny inference of closely related bacterial species. However, the inference may experience a decrease in performance for phylogenomic analysis of too closely or too distantly related genomes. Another drawback of OGs as phylogenetic markers is that they usually take little account of the effects of genomic rearrangement on the similarity estimation, such as intra-chromosome/genome translocations, horizontal gene transfer, and gene losses. To explore such effects on the accuracy of phylogeny reconstruction, we combine phylogenetic signals of OGs with collinear genomic regions, here called locally collinear blocks (LCBs). By putting these together, we refine our previous metric of pairwise similarity between two closely related bacterial genomes. As a case study, we used this new method to reconstruct the phylogenies of 88 Enterobacteriale genomes of the class Gammaproteobacteria. Our results demonstrated that the topological accuracy of the inferred phylogeny was improved when both OGs and LCBs were simultaneously considered, suggesting that combining these two phylogenetic markers may reduce, to some extent, the influence of gene loss on phylogeny inference. Such phylogenomic studies, we believe, will help us to explore a more effective approach to increasing the robustness of phylogeny reconstruction of closely related bacterial organisms. PMID:26715828

  5. Spatially explicit inference for open populations: estimating demographic parameters from camera-trap studies.

    Science.gov (United States)

    Gardner, Beth; Reppucci, Juan; Lucherini, Mauro; Royle, J Andrew

    2010-11-01

    We develop a hierarchical capture-recapture model for demographically open populations when auxiliary spatial information about location of capture is obtained. Such spatial capture-recapture data arise from studies based on camera trapping, DNA sampling, and other situations in which a spatial array of devices records encounters of unique individuals. We integrate an individual-based formulation of a Jolly-Seber type model with recently developed spatially explicit capture-recapture models to estimate density and demographic parameters for survival and recruitment. We adopt a Bayesian framework for inference under this model using the method of data augmentation which is implemented in the software program WinBUGS. The model was motivated by a camera trapping study of Pampas cats Leopardus colocolo from Argentina, which we present as an illustration of the model in this paper. We provide estimates of density and the first quantitative assessment of vital rates for the Pampas cat in the High Andes. The precision of these estimates is poor due likely to the sparse data set. Unlike conventional inference methods which usually rely on asymptotic arguments, Bayesian inferences are valid in arbitrary sample sizes, and thus the method is ideal for the study of rare or endangered species for which small data sets are typical.

  6. Evaluating species richness: biased ecological inference results from spatial heterogeneity in species detection probabilities

    Science.gov (United States)

    McNew, Lance B.; Handel, Colleen M.

    2015-01-01

    Accurate estimates of species richness are necessary to test predictions of ecological theory and evaluate biodiversity for conservation purposes. However, species richness is difficult to measure in the field because some species will almost always be overlooked due to their cryptic nature or the observer's failure to perceive their cues. Common measures of species richness that assume consistent observability across species are inviting because they may require only single counts of species at survey sites. Single-visit estimation methods ignore spatial and temporal variation in species detection probabilities related to survey or site conditions that may confound estimates of species richness. We used simulated and empirical data to evaluate the bias and precision of raw species counts, the limiting forms of jackknife and Chao estimators, and multi-species occupancy models when estimating species richness to evaluate whether the choice of estimator can affect inferences about the relationships between environmental conditions and community size under variable detection processes. Four simulated scenarios with realistic and variable detection processes were considered. Results of simulations indicated that (1) raw species counts were always biased low, (2) single-visit jackknife and Chao estimators were significantly biased regardless of detection process, (3) multispecies occupancy models were more precise and generally less biased than the jackknife and Chao estimators, and (4) spatial heterogeneity resulting from the effects of a site covariate on species detection probabilities had significant impacts on the inferred relationships between species richness and a spatially explicit environmental condition. For a real dataset of bird observations in northwestern Alaska, the four estimation methods produced different estimates of local species richness, which severely affected inferences about the effects of shrubs on local avian richness. Overall, our results

  7. Local temperatures inferred from plant communities suggest strong spatial buffering of climate warming across Northern Europe

    DEFF Research Database (Denmark)

    Lenoir, Jonathan; Graae, Bente; Aarrestad, Per

    2013-01-01

    -change impacts. Is this local spatial buffering restricted to topographically complex terrains? To answer this, we here study fine-grained thermal variability across a 2500-km wide latitudinal gradient in Northern Europe encompassing a large array of topographic complexities. We first combined plant community...... data, Ellenberg temperature indicator values, locally measured temperatures (LmT) and globally interpolated temperatures (GiT) in a modelling framework to infer biologically relevant temperature conditions from plant assemblages within community-inferred temperatures: CiT). We...... temperature indicator values in combination with plant assemblages explained 46-72% of variation in LmT and 92-96% of variation in GiT during the growing season (June, July, August). Growing-season CiT range within 1-km(2) units peaked at 60-65°N and increased with terrain roughness, averaging 1.97 °C (SD = 0...

  8. The effects of spatial autoregressive dependencies on inference in ordinary least squares: a geometric approach

    Science.gov (United States)

    Smith, Tony E.; Lee, Ka Lok

    2012-01-01

    There is a common belief that the presence of residual spatial autocorrelation in ordinary least squares (OLS) regression leads to inflated significance levels in beta coefficients and, in particular, inflated levels relative to the more efficient spatial error model (SEM). However, our simulations show that this is not always the case. Hence, the purpose of this paper is to examine this question from a geometric viewpoint. The key idea is to characterize the OLS test statistic in terms of angle cosines and examine the geometric implications of this characterization. Our first result is to show that if the explanatory variables in the regression exhibit no spatial autocorrelation, then the distribution of test statistics for individual beta coefficients in OLS is independent of any spatial autocorrelation in the error term. Hence, inferences about betas exhibit all the optimality properties of the classic uncorrelated error case. However, a second more important series of results show that if spatial autocorrelation is present in both the dependent and explanatory variables, then the conventional wisdom is correct. In particular, even when an explanatory variable is statistically independent of the dependent variable, such joint spatial dependencies tend to produce "spurious correlation" that results in over-rejection of the null hypothesis. The underlying geometric nature of this problem is clarified by illustrative examples. The paper concludes with a brief discussion of some possible remedies for this problem.

  9. Exploration into the spatial and temporal mechanisms of bacterial polarity

    DEFF Research Database (Denmark)

    Ebersbach, Gitte; Jacobs-Wagner, Christine; Charbon, Gitte Ebersbach

    2007-01-01

    The recognition of bacterial asymmetry is not new: the first high-resolution microscopy studies revealed that bacteria come in a multitude of shapes and sometimes carry asymmetrically localized external structures such as flagella on the cell surface. Even so, the idea that bacteria could have...... polarity in bacteria....

  10. BPhyOG: An interactive server for genome-wide inference of bacterial phylogenies based on overlapping genes

    Directory of Open Access Journals (Sweden)

    Lin Kui

    2007-07-01

    Full Text Available Abstract Background Overlapping genes (OGs in bacterial genomes are pairs of adjacent genes of which the coding sequences overlap partly or entirely. With the rapid accumulation of sequence data, many OGs in bacterial genomes have now been identified. Indeed, these might prove a consistent feature across all microbial genomes. Our previous work suggests that OGs can be considered as robust markers at the whole genome level for the construction of phylogenies. An online, interactive web server for inferring phylogenies is needed for biologists to analyze phylogenetic relationships among a set of bacterial genomes of interest. Description BPhyOG is an online interactive server for reconstructing the phylogenies of completely sequenced bacterial genomes on the basis of their shared overlapping genes. It provides two tree-reconstruction methods: Neighbor Joining (NJ and Unweighted Pair-Group Method using Arithmetic averages (UPGMA. Users can apply the desired method to generate phylogenetic trees, which are based on an evolutionary distance matrix for the selected genomes. The distance between two genomes is defined by the normalized number of their shared OG pairs. BPhyOG also allows users to browse the OGs that were used to infer the phylogenetic relationships. It provides detailed annotation for each OG pair and the features of the component genes through hyperlinks. Users can also retrieve each of the homologous OG pairs that have been determined among 177 genomes. It is a useful tool for analyzing the tree of life and overlapping genes from a genomic standpoint. Conclusion BPhyOG is a useful interactive web server for genome-wide inference of any potential evolutionary relationship among the genomes selected by users. It currently includes 177 completely sequenced bacterial genomes containing 79,855 OG pairs, the annotation and homologous OG pairs of which are integrated comprehensively. The reliability of phylogenies complemented by

  11. Spatial pattern in Antarctica: what can we learn from Antarctic bacterial isolates?

    Science.gov (United States)

    Chong, Chun Wie; Goh, Yuh Shan; Convey, Peter; Pearce, David; Tan, Irene Kit Ping

    2013-09-01

    A range of small- to moderate-scale studies of patterns in bacterial biodiversity have been conducted in Antarctica over the last two decades, most suggesting strong correlations between the described bacterial communities and elements of local environmental heterogeneity. However, very few of these studies have advanced interpretations in terms of spatially associated patterns, despite increasing evidence of patterns in bacterial biogeography globally. This is likely to be a consequence of restricted sampling coverage, with most studies to date focusing only on a few localities within a specific Antarctic region. Clearly, there is now a need for synthesis over a much larger spatial to consolidate the available data. In this study, we collated Antarctic bacterial culture identities based on the 16S rRNA gene information available in the literature and the GenBank database (n > 2,000 sequences). In contrast to some recent evidence for a distinct Antarctic microbiome, our phylogenetic comparisons show that a majority (~75 %) of Antarctic bacterial isolates were highly similar (≥99 % sequence similarity) to those retrieved from tropical and temperate regions, suggesting widespread distribution of eurythermal mesophiles in Antarctic environments. However, across different Antarctic regions, the dominant bacterial genera exhibit some spatially distinct diversity patterns analogous to those recently proposed for Antarctic terrestrial macroorganisms. Taken together, our results highlight the threat of cross-regional homogenisation in Antarctic biodiversity, and the imperative to include microbiota within the framework of biosecurity measures for Antarctica.

  12. Inferring reputation promotes the evolution of cooperation in spatial social dilemma games.

    Directory of Open Access Journals (Sweden)

    Zhen Wang

    Full Text Available In realistic world individuals with high reputation are more likely to influence the collective behaviors. Due to the cost and error of information dissemination, however, it is unreasonable to assign each individual with a complete cognitive power, which means that not everyone can accurately realize others' reputation situation. Here we introduce the mechanism of inferring reputation into the selection of potential strategy sources to explore the evolution of cooperation. Before the game each player is assigned with a randomly distributed parameter p denoting his ability to infer the reputation of others. The parameter p of each individual is kept constant during the game. The value of p indicates that the neighbor possessing highest reputation is chosen with the probability p and randomly choosing an opponent is left with the probability 1-p. We find that this novel mechanism can be seen as an universally applicable promoter of cooperation, which works on various interaction networks and in different types of evolutionary game. Of particular interest is the fact that, in the early stages of evolutionary process, cooperators with high reputation who are easily regarded as the potential strategy donors can quickly lead to the formation of extremely robust clusters of cooperators that are impervious to defector attacks. These clusters eventually help cooperators reach their undisputed dominance, which transcends what can be warranted by the spatial reciprocity alone. Moreover, we provide complete phase diagrams to depict the impact of uncertainty in strategy adoptions and conclude that the effective interaction topology structure may be altered under such a mechanism. When the estimation of reputation is extended, we also show that the moderate value of evaluation factor enables cooperation to thrive best. We thus present a viable method of understanding the ubiquitous cooperative behaviors in nature and hope that it will inspire further studies

  13. Temporal and Spatial Diversity of Bacterial Communities in Coastal Waters of the South China Sea

    Science.gov (United States)

    Du, Jikun; Xiao, Kai; Li, Li; Ding, Xian; Liu, Helu; Lu, Yongjun; Zhou, Shining

    2013-01-01

    Bacteria are recognized as important drivers of biogeochemical processes in all aquatic ecosystems. Temporal and geographical patterns in ocean bacterial communities have been observed in many studies, but the temporal and spatial patterns in the bacterial communities from the South China Sea remained unexplored. To determine the spatiotemporal patterns, we generated 16S rRNA datasets for 15 samples collected from the five regularly distributed sites of the South China Sea in three seasons (spring, summer, winter). A total of 491 representative sequences were analyzed by MOTHUR, yielding 282 operational taxonomic units (OTUs) grouped at 97% stringency. Significant temporal variations of bacterial diversity were observed. Richness and diversity indices indicated that summer samples were the most diverse. The main bacterial group in spring and summer samples was Alphaproteobacteria, followed by Cyanobacteria and Gammaproteobacteria, whereas Cyanobacteria dominated the winter samples. Spatial patterns in the samples were observed that samples collected from the coastal (D151, D221) waters and offshore (D157, D1512, D224) waters clustered separately, the coastal samples harbored more diverse bacterial communities. However, the temporal pattern of the coastal site D151 was contrary to that of the coastal site D221. The LIBSHUFF statistics revealed noticeable differences among the spring, summer and winter libraries collected at five sites. The UPGMA tree showed there were temporal and spatial heterogeneity of bacterial community composition in coastal waters of the South China Sea. The water salinity (P=0.001) contributed significantly to the bacteria-environment relationship. Our results revealed that bacterial community structures were influenced by environmental factors and community-level changes in 16S-based diversity were better explained by spatial patterns than by temporal patterns. PMID:23785512

  14. The impact of natural transformation on adaptation in spatially structured bacterial populations.

    Science.gov (United States)

    Moradigaravand, Danesh; Engelstädter, Jan

    2014-06-20

    Recent studies have demonstrated that natural transformation and the formation of highly structured populations in bacteria are interconnected. In spite of growing evidence about this connection, little is known about the dynamics of natural transformation in spatially structured bacterial populations. In this work, we model the interdependency between the dynamics of the bacterial gene pool and those of environmental DNA in space to dissect the effect of transformation on adaptation. Our model reveals that even with only a single locus under consideration, transformation with a free DNA fragment pool results in complex adaptation dynamics that do not emerge in previous models focusing only on the gene shuffling effect of transformation at multiple loci. We demonstrate how spatial restriction on population growth and DNA diffusion in the environment affect the impact of transformation on adaptation. We found that in structured bacterial populations intermediate DNA diffusion rates predominantly cause transformation to impede adaptation by spreading deleterious alleles in the population. Overall, our model highlights distinctive evolutionary consequences of bacterial transformation in spatially restricted compared to planktonic bacterial populations.

  15. Spatial variability of coastal wetland resilience to sea-level rise using Bayesian inference

    Science.gov (United States)

    Hardy, T.; Wu, W.

    2017-12-01

    The coastal wetlands in the Northern Gulf of Mexico (NGOM) account for 40% of coastal wetland area in the United States and provide various ecosystem services to the region and broader areas. Increasing rates of relative sea-level rise (RSLR), and reduced sediment input have increased coastal wetland loss in the NGOM, accounting for 80% of coastal wetland loss in the nation. Traditional models for predicting the impact of RSLR on coastal wetlands in the NGOM have focused on coastal erosion driven by geophysical variables only, and/or at small spatial extents. Here we developed a model in Bayesian inference to make probabilistic prediction of wetland loss in the entire NGOM as a function of vegetation productivity and geophysical attributes. We also studied how restoration efforts help maintain the area of coastal wetlands. Vegetation productivity contributes organic matter to wetland sedimentation and was approximated using the remotely sensed normalized difference moisture index (NDMI). The geophysical variables include RSLR, tidal range, river discharge, coastal slope, and wave height. We found a significantly positive relation between wetland loss and RSLR, which varied significantly at different river discharge regimes. There also existed a significantly negative relation between wetland loss and NDMI, indicating that in-situ vegetation productivity contributed to wetland resilience to RSLR. This relation did not vary significantly between river discharge regimes. The spatial relation revealed three areas of high RSLR but relatively low wetland loss; these areas were associated with wetland restoration projects in coastal Louisiana. Two projects were breakwater projects, where hard materials were placed off-shore to reduce wave action and promote sedimentation. And one project was a vegetation planting project used to promote sedimentation and wetland stabilization. We further developed an interactive web tool that allows stakeholders to develop similar wetland

  16. Hydrologic linkages drive spatial structuring of bacterial assemblages and functioning in alpine floodplains

    OpenAIRE

    Freimann, Remo; Bürgmann, Helmut; Findlay, Stuart E.G.; Robinson, Christopher T.

    2015-01-01

    Microbial community assembly and microbial functions are affected by a number of different but coupled drivers such as local habitat characteristics, dispersal rates, and species interactions. In groundwater systems, hydrological flow can introduce spatial structure and directional dependencies among these drivers. We examined the importance of hydrology in structuring bacterial communities and their function within two alpine floodplains during different hydrological states. Piezometers were...

  17. Contrasting spatial patterns and ecological attributes of soil bacterial and archaeal taxa across a landscape.

    Science.gov (United States)

    Constancias, Florentin; Saby, Nicolas P A; Terrat, Sébastien; Dequiedt, Samuel; Horrigue, Wallid; Nowak, Virginie; Guillemin, Jean-Philippe; Biju-Duval, Luc; Chemidlin Prévost-Bouré, Nicolas; Ranjard, Lionel

    2015-06-01

    Even though recent studies have clarified the influence and hierarchy of environmental filters on bacterial community structure, those constraining bacterial populations variations remain unclear. In consequence, our ability to understand to ecological attributes of soil bacteria and to predict microbial community response to environmental stress is therefore limited. Here, we characterized the bacterial community composition and the various bacterial taxonomic groups constituting the community across an agricultural landscape of 12 km(2) , by using a 215 × 215 m systematic grid representing 278 sites to precisely decipher their spatial distribution and drivers at this scale. The bacterial and Archaeal community composition was characterized by applying 16S rRNA gene pyrosequencing directly to soil DNA from samples. Geostatistics tools were used to reveal the heterogeneous distribution of bacterial composition at this scale. Soil physical parameters and land management explained a significant amount of variation, suggesting that environmental selection is the major process shaping bacterial composition. All taxa systematically displayed also a heterogeneous and particular distribution patterns. Different relative influences of soil characteristics, land use and space were observed, depending on the taxa, implying that selection and spatial processes might be differentially but not exclusively involved for each bacterial phylum. Soil pH was a major factor determining the distribution of most of the bacterial taxa and especially the most important factor explaining the spatial patterns of α-Proteobacteria and Planctomycetes. Soil texture, organic carbon content and quality were more specific to a few number of taxa (e.g., β-Proteobacteria and Chlorobi). Land management also influenced the distribution of bacterial taxa across the landscape and revealed different type of response to cropping intensity (positive, negative, neutral or hump-backed relationships

  18. Seasonal and spatial patterns of heterotrophic bacterial production, respiration, and biomass in the subarctic NE Pacific

    Science.gov (United States)

    Sherry, Nelson D.; Boyd, Philip W.; Sugimoto, Kugako; Harrison, Paul J.

    1999-11-01

    Heterotrophic bacterial biomass, production, and respiration rates were measured during winter, spring, and summer in the subarctic NE Pacific from September 1995 to June 1997. Sampling took place on six cruises at five hydrographic stations along the east/west line-P transect from slope waters at P4 (1200 m depth) to the open-ocean waters at Ocean Station Papa (OSP) (4250 m depth). Interannual variability was small relative to seasonal and spatial variability. Biomass, derived from cell counts (assuming 20 fg C cell -1), was ca. 12 μg C l -1 in the winter and increased to 20-35 μg C l -1 in the spring and summer all along line-P. Bacterial production from [ 3H]-thymidine and [ 14C]-leucine incorporation rates was lowest in the winter (ca. 0.5 μg C l -1 d -1) with little spatial variability. Production increased 10-fold in spring at P4 (to ca. 4.5 μg C l -1 d -1). In contrast, only a 2-fold increase in bacterial production was observed over this period at the more oceanic stations. Rates of production in late summer were highest over the annual cycle at all stations ranging from ca. 6 at P4 to ca. 2 μg C l -1 d -1 at OSP. Bacterial (rates increased >10-fold to ca. 100 μg C l -1 d -1 at P4 in the summer, but, interestingly, did not increase from spring to summer at the more oceanic stations. Thus bacterial growth efficiency, defined as production/(production+respiration), decreased in the spring westwards from the slope waters (P4) to the open-ocean (OSP), but increased westwards in the summer. Bacterial production was highly correlated with temperature at OSP ( r2=0.88) and less so at P4 ( r2=0.50). The observed temporal and spatial trends presented in this study suggest that seasonal changes in bacterial biomass were greatly affected by changes in loss processes, that bacterial biomass is regulated by different processes than bacterial production, and that bacterial production alone, without respiration measurements, is not a robust proxy for bacterial

  19. Spatial and vertical distribution of bacterial community in the northern South China Sea.

    Science.gov (United States)

    Sun, Fu-Lin; Wang, You-Shao; Wu, Mei-Lin; Sun, Cui-Ci; Cheng, Hao

    2015-10-01

    Microbial communities are highly diverse in coastal oceans and response rapidly with changing environments. Learning about this will help us understand the ecology of microbial populations in marine ecosystems. This study aimed to assess the spatial and vertical distributions of the bacterial community in the northern South China Sea. Multi-dimensional scaling analyses revealed structural differences of the bacterial community among sampling sites and vertical depth. Result also indicated that bacterial community in most sites had higher diversity in 0-75 m depths than those in 100-200 m depths. Bacterial community of samples was positively correlation with salinity and depth, whereas was negatively correlation with temperature. Proteobacteria and Cyanobacteria were the dominant groups, which accounted for the majority of sequences. The α-Proteobacteria was highly diverse, and sequences belonged to Rhodobacterales bacteria were dominant in all characterized sequences. The current data indicate that the Rhodobacterales bacteria, especially Roseobacter clade are the diverse group in the tropical waters.

  20. Seasonal and spatial variation of bacterial production and abundance in the northern Levantine Sea

    Directory of Open Access Journals (Sweden)

    N. YUCEL

    2017-03-01

    Full Text Available Spatial and temporal heterogeneity in bacterial production and abundance in relation to ambient bio-physicochemical parameters has been investigated in the Levantine Sea. Five stations with different trophic states in an area extending from highly eutrophic Mersin bay to the mesotrophic Rhodes gyre area including the oligotrophic offshore waters were sampled four times. Integrated bacterial production varied between 6.1 and 90.3 µg C m-2 d-1 with higher rates occurring during September 2012 in offshore waters. Bacterial abundance ranged between 0.18 and 7.3 x 105 cells ml-1 within the euphotic zone and was generally higher up to 100 meters throughout the study period. In offshore waters, bacterial production (0.401 to 0.050 µg C m-3 d-1, abundance (4.5 to 1.6 x 105 cells ml-1 and depth of the productive layer decreased from 150 to 75 meters westward along the transect. Although the highest abundance was observed in July 2012 in offshore waters, the highest activity was measured in September 2012. These results indicated that the temperature played a key role in regulating bacterial abundance and production in the area. High chlorophyll concentrations in March did not correspond to high bacterial abundance and production at the same time. Increase in dissolved organic carbon content following spring phytoplankton bloom and the increase in temperature in the mean time might have enhanced the bacterial activity towards summer.

  1. Interaction of Uranium with Bacterial Cell Surfaces: Inferences from Phosphatase-Mediated Uranium Precipitation

    Science.gov (United States)

    Kulkarni, Sayali; Misra, Chitra Seetharam; Gupta, Alka; Ballal, Anand

    2016-01-01

    ABSTRACT Deinococcus radiodurans and Escherichia coli expressing either PhoN, a periplasmic acid phosphatase, or PhoK, an extracellular alkaline phosphatase, were evaluated for uranium (U) bioprecipitation under two specific geochemical conditions (GCs): (i) a carbonate-deficient condition at near-neutral pH (GC1), and (ii) a carbonate-abundant condition at alkaline pH (GC2). Transmission electron microscopy revealed that recombinant cells expressing PhoN/PhoK formed cell-associated uranyl phosphate precipitate under GC1, whereas the same cells displayed extracellular precipitation under GC2. These results implied that the cell-bound or extracellular location of the precipitate was governed by the uranyl species prevalent at that particular GC, rather than the location of phosphatase. MINTEQ modeling predicted the formation of predominantly positively charged uranium hydroxide ions under GC1 and negatively charged uranyl carbonate-hydroxide complexes under GC2. Both microbes adsorbed 6- to 10-fold more U under GC1 than under GC2, suggesting that higher biosorption of U to the bacterial cell surface under GC1 may lead to cell-associated U precipitation. In contrast, at alkaline pH and in the presence of excess carbonate under GC2, poor biosorption of negatively charged uranyl carbonate complexes on the cell surface might have resulted in extracellular precipitation. The toxicity of U observed under GC1 being higher than that under GC2 could also be attributed to the preferential adsorption of U on cell surfaces under GC1. This work provides a vivid description of the interaction of U complexes with bacterial cells. The findings have implications for the toxicity of various U species and for developing biological aqueous effluent waste treatment strategies. IMPORTANCE The present study provides illustrative insights into the interaction of uranium (U) complexes with recombinant bacterial cells overexpressing phosphatases. This work demonstrates the effects of aqueous

  2. Ancestry inference using principal component analysis and spatial analysis: a distance-based analysis to account for population substructure.

    Science.gov (United States)

    Byun, Jinyoung; Han, Younghun; Gorlov, Ivan P; Busam, Jonathan A; Seldin, Michael F; Amos, Christopher I

    2017-10-16

    Accurate inference of genetic ancestry is of fundamental interest to many biomedical, forensic, and anthropological research areas. Genetic ancestry memberships may relate to genetic disease risks. In a genome association study, failing to account for differences in genetic ancestry between cases and controls may also lead to false-positive results. Although a number of strategies for inferring and taking into account the confounding effects of genetic ancestry are available, applying them to large studies (tens thousands samples) is challenging. The goal of this study is to develop an approach for inferring genetic ancestry of samples with unknown ancestry among closely related populations and to provide accurate estimates of ancestry for application to large-scale studies. In this study we developed a novel distance-based approach, Ancestry Inference using Principal component analysis and Spatial analysis (AIPS) that incorporates an Inverse Distance Weighted (IDW) interpolation method from spatial analysis to assign individuals to population memberships. We demonstrate the benefits of AIPS in analyzing population substructure, specifically related to the four most commonly used tools EIGENSTRAT, STRUCTURE, fastSTRUCTURE, and ADMIXTURE using genotype data from various intra-European panels and European-Americans. While the aforementioned commonly used tools performed poorly in inferring ancestry from a large number of subpopulations, AIPS accurately distinguished variations between and within subpopulations. Our results show that AIPS can be applied to large-scale data sets to discriminate the modest variability among intra-continental populations as well as for characterizing inter-continental variation. The method we developed will protect against spurious associations when mapping the genetic basis of a disease. Our approach is more accurate and computationally efficient method for inferring genetic ancestry in the large-scale genetic studies.

  3. Spatially resolved characterization of biogenic manganese oxideproduction within a bacterial biofilm

    Energy Technology Data Exchange (ETDEWEB)

    Toner, Brandy; Fakra, Sirine; Villalobos, Mario; Warwick, Tony; Sposito, Garrison

    2004-10-01

    Pseudomonas putida strain MnB1, a biofilm forming bacteria, was used as a model for the study of bacterial Mn oxidation in freshwater and soil environments. The oxidation of Mn{sub (aq)}{sup +2} by P. putida was characterized by spatially and temporally resolving the oxidation state of Mn in the presence of a bacterial biofilm using scanning transmission x-ray microscopy (STXM) combined with near edge x-ray absorption fine structure (NEXAFS) spectroscopy at the Mn-L{sub 2,3} absorption edges. Subsamples were collected from growth flasks containing 0.1 mM and 1 mM total Mn at 16, 24, 36 and 48 hours after inoculation. Immediately after collection, the unprocessed hydrated subsamples were imaged at 40 nm resolution. Manganese NEXAFS spectra were extracted from x-ray energy sequences of STXM images (stacks) and fit with linear combinations of well characterized reference spectra to obtain quantitative relative abundances of Mn(II), Mn(III) and Mn(IV). Careful consideration was given to uncertainty in the normalization of the reference spectra, choice of reference compounds, and chemical changes due to radiation damage. The STXM results confirm that Mn{sub (aq)}{sup +2} was removed from solution by P. putida and was concentrated as Mn(III) and Mn(IV) immediately adjacent to the bacterial cells. The Mn precipitates were completely enveloped by bacterial biofilm material. The distribution of Mn oxidation states was spatially heterogeneous within and between the clusters of bacterial cells. Scanning transmission x-ray microscopy is a promising tool to advance the study of hydrated interfaces between minerals and bacteria, particularly in cases where the structure of bacterial biofilms needs to be maintained.

  4. Cell shape can mediate the spatial organization of the bacterial cytoskeleton

    Science.gov (United States)

    Wang, Siyuan; Wingreen, Ned

    2013-03-01

    The bacterial cytoskeleton guides the synthesis of cell wall and thus regulates cell shape. Since spatial patterning of the bacterial cytoskeleton is critical to the proper control of cell shape, it is important to ask how the cytoskeleton spatially self-organizes in the first place. In this work, we develop a quantitative model to account for the various spatial patterns adopted by bacterial cytoskeletal proteins, especially the orientation and length of cytoskeletal filaments such as FtsZ and MreB in rod-shaped cells. We show that the combined mechanical energy of membrane bending, membrane pinning, and filament bending of a membrane-attached cytoskeletal filament can be sufficient to prescribe orientation, e.g. circumferential for FtsZ or helical for MreB, with the accuracy of orientation increasing with the length of the cytoskeletal filament. Moreover, the mechanical energy can compete with the chemical energy of cytoskeletal polymerization to regulate filament length. Notably, we predict a conformational transition with increasing polymer length from smoothly curved to end-bent polymers. Finally, the mechanical energy also results in a mutual attraction among polymers on the same membrane, which could facilitate tight polymer spacing or bundling. The predictions of the model can be verified through genetic, microscopic, and microfluidic approaches.

  5. Accounting for the measurement error of spectroscopically inferred soil carbon data for improved precision of spatial predictions.

    Science.gov (United States)

    Somarathna, P D S N; Minasny, Budiman; Malone, Brendan P; Stockmann, Uta; McBratney, Alex B

    2018-08-01

    Spatial modelling of environmental data commonly only considers spatial variability as the single source of uncertainty. In reality however, the measurement errors should also be accounted for. In recent years, infrared spectroscopy has been shown to offer low cost, yet invaluable information needed for digital soil mapping at meaningful spatial scales for land management. However, spectrally inferred soil carbon data are known to be less accurate compared to laboratory analysed measurements. This study establishes a methodology to filter out the measurement error variability by incorporating the measurement error variance in the spatial covariance structure of the model. The study was carried out in the Lower Hunter Valley, New South Wales, Australia where a combination of laboratory measured, and vis-NIR and MIR inferred topsoil and subsoil soil carbon data are available. We investigated the applicability of residual maximum likelihood (REML) and Markov Chain Monte Carlo (MCMC) simulation methods to generate parameters of the Matérn covariance function directly from the data in the presence of measurement error. The results revealed that the measurement error can be effectively filtered-out through the proposed technique. When the measurement error was filtered from the data, the prediction variance almost halved, which ultimately yielded a greater certainty in spatial predictions of soil carbon. Further, the MCMC technique was successfully used to define the posterior distribution of measurement error. This is an important outcome, as the MCMC technique can be used to estimate the measurement error if it is not explicitly quantified. Although this study dealt with soil carbon data, this method is amenable for filtering the measurement error of any kind of continuous spatial environmental data. Copyright © 2018 Elsevier B.V. All rights reserved.

  6. What causes the spatial heterogeneity of bacterial flora in the intestine of zebrafish larvae?

    Science.gov (United States)

    Yang, Jinyou; Shimogonya, Yuji; Ishikawa, Takuji

    2018-06-07

    Microbial flora in the intestine has been thoroughly investigated, as it plays an important role in the health of the host. Jemielita et al. (2014) showed experimentally that Aeromonas bacteria in the intestine of zebrafish larvae have a heterogeneous spatial distribution. Although bacterial aggregation is important biologically and clinically, there is no mathematical model describing the phenomenon and its mechanism remains largely unknown. In this study, we developed a computational model to describe the heterogeneous distribution of bacteria in the intestine of zebrafish larvae. The results showed that biological taxis could cause the bacterial aggregation. Intestinal peristalsis had the effect of reducing bacterial aggregation through mixing function. Using a scaling argument, we showed that the taxis velocity of bacteria must be larger than the sum of the diffusive velocity and background bulk flow velocity to induce bacterial aggregation. Our model and findings will be useful to further the scientific understanding of intestinal microbial flora. Copyright © 2018 Elsevier Ltd. All rights reserved.

  7. Spatial Patterning of Newly-Inserted Material during Bacterial Cell Growth

    Science.gov (United States)

    Ursell, Tristan

    2012-02-01

    In the life cycle of a bacterium, rudimentary microscopy demonstrates that cell growth and elongation are essential characteristics of cellular reproduction. The peptidoglycan cell wall is the main load-bearing structure that determines both cell shape and overall size. However, simple imaging of cellular growth gives no indication of the spatial patterning nor mechanism by which material is being incorporated into the pre-existing cell wall. We employ a combination of high-resolution pulse-chase fluorescence microscopy, 3D computational microscopy, and detailed mechanistic simulations to explore how spatial patterning results in uniform growth and maintenance of cell shape. We show that growth is happening in discrete bursts randomly distributed over the cell surface, with a well-defined mean size and average rate. We further use these techniques to explore the effects of division and cell wall disrupting antibiotics, like cephalexin and A22, respectively, on the patterning of cell wall growth in E. coli. Finally, we explore the spatial correlation between presence of the bacterial actin-like cytoskeletal protein, MreB, and local cell wall growth. Together these techniques form a powerful method for exploring the detailed dynamics and involvement of antibiotics and cell wall-associated proteins in bacterial cell growth.[4pt] In collaboration with Kerwyn Huang, Stanford University.

  8. Spatial scaling of bacterial community diversity at shallow hydrothermal vents: a global comparison

    Science.gov (United States)

    Pop Ristova, P.; Hassenrueck, C.; Molari, M.; Fink, A.; Bühring, S. I.

    2016-02-01

    Marine shallow hydrothermal vents are extreme environments, often characterized by discharge of fluids with e.g. high temperatures, low pH, and laden with elements toxic to higher organisms. They occur at continental margins around the world's oceans, but represent fragmented, isolated habitats of locally small areal coverage. Microorganisms contribute the main biomass at shallow hydrothermal vent ecosystems and build the basis of the food chain by autotrophic fixation of carbon both via chemosynthesis and photosynthesis, occurring simultaneously. Despite their importance and unique capacity to adapt to these extreme environments, little is known about the spatial scales on which the alpha- and beta-diversity of microbial communities vary at shallow vents, and how the geochemical habitat heterogeneity influences shallow vent biodiversity. Here for the first time we investigated the spatial scaling of microbial biodiversity patterns and their interconnectivity at geochemically diverse shallow vents on a global scale. This study presents data on the comparison of bacterial community structures on large (> 1000 km) and small (0.1 - 100 m) spatial scales as derived from ARISA and Illumina sequencing. Despite the fragmented global distribution of shallow hydrothermal vents, similarity of vent bacterial communities decreased with geographic distance, confirming the ubiquity of distance-decay relationship. Moreover, at all investigated vents, pH was the main factor locally structuring these communities, while temperature influenced both the alpha- and beta-diversity.

  9. Toward Bayesian inference of the spatial distribution of proteins from three-cube Förster resonance energy transfer data

    DEFF Research Database (Denmark)

    Hooghoudt, Jan Otto; Barroso, Margarida; Waagepetersen, Rasmus Plenge

    2017-01-01

    Főrster resonance energy transfer (FRET) is a quantum-physical phenomenon where energy may be transferred from one molecule to a neighbour molecule if the molecules are close enough. Using fluorophore molecule marking of proteins in a cell it is possible to measure in microscopic images to what....... In this paper we propose a new likelihood-based approach to statistical inference for FRET microscopic data. The likelihood function is obtained from a detailed modeling of the FRET data generating mechanism conditional on a protein configuration. We next follow a Bayesian approach and introduce a spatial point...

  10. Spatial distribution of planktonic bacterial and archaeal communities in the upper section of the tidal reach in Yangtze River

    Science.gov (United States)

    Fan, Limin; Song, Chao; Meng, Shunlong; Qiu, Liping; Zheng, Yao; Wu, Wei; Qu, Jianhong; Li, Dandan; Zhang, Cong; Hu, Gengdong; Chen, Jiazhang

    2016-01-01

    Bacterioplankton and archaeaplankton communities play key roles in the biogeochemical processes of water, and they may be affected by many factors. In this study, we used high-throughput 16S rRNA gene sequencing to profile planktonic bacterial and archaeal community compositions in the upper section of the tidal reach in Yangtze River. We found that the predominant bacterial phyla in this river section were Proteobacteria, Firmicutes, and Actinobacteria, whereas the predominant archaeal classes were Halobacteria, Methanomicrobia, and unclassified Euryarchaeota. Additionally, the bacterial and archaeal community compositions, richnesses, functional profiles, and ordinations were affected by the spatial heterogeneity related to the concentration changes of sulphate or nitrate. Notably, the bacterial community was more sensitive than the archaeal community to changes in the spatial characteristics of this river section. These findings provide important insights into the distributions of bacterial and archaeal communities in natural water habitats. PMID:27966673

  11. Penultimate modeling of spatial extremes: statistical inference for max-infinitely divisible processes

    KAUST Repository

    Huser, Raphaë l; Opitz, Thomas; Thibaud, Emeric

    2018-01-01

    Extreme-value theory for stochastic processes has motivated the statistical use of max-stable models for spatial extremes. However, fitting such asymptotic models to maxima observed over finite blocks is problematic when the asymptotic stability

  12. From GPS tracks to context: Inference of high-level context information through spatial clustering

    OpenAIRE

    Moreira, Adriano; Santos, Maribel Yasmina

    2005-01-01

    Location-aware applications use the location of users to adapt their behaviour and to select the relevant information for users in a particular situation. This location information is obtained through a set of location sensors, or from network-based location services, and is often used directly, without any further processing, as a parameter in a selection process. In this paper we propose a method to infer high-level context information from a series of position records obtained from a GPS r...

  13. Temporal and Spatial Variations of Bacterial and Faunal Communities Associated with Deep-Sea Wood Falls

    Science.gov (United States)

    Bienhold, Christina; Wenzhöfer, Frank; Rossel, Pamela E.; Boetius, Antje

    2017-01-01

    Sinking of large organic food falls i.e. kelp, wood and whale carcasses to the oligotrophic deep-sea floor promotes the establishment of locally highly productive and diverse ecosystems, often with specifically adapted benthic communities. However, the fragmented spatial distribution and small area poses challenges for the dispersal of their microbial and faunal communities. Our study focused on the temporal dynamics and spatial distributions of sunken wood bacterial communities, which were deployed in the vicinity of different cold seeps in the Eastern Mediterranean and the Norwegian deep-seas. By combining fingerprinting of bacterial communities by ARISA and 454 sequencing with in situ and ex situ biogeochemical measurements, we show that sunken wood logs have a locally confined long-term impact (> 3y) on the sediment geochemistry and community structure. We confirm previous hypotheses of different successional stages in wood degradation including a sulphophilic one, attracting chemosynthetic fauna from nearby seep systems. Wood experiments deployed at similar water depths (1100–1700 m), but in hydrographically different oceanic regions harbored different wood-boring bivalves, opportunistic faunal communities, and chemosynthetic species. Similarly, bacterial communities on sunken wood logs were more similar within one geographic region than between different seas. Diverse sulphate-reducing bacteria of the Deltaproteobacteria, the sulphide-oxidizing bacteria Sulfurovum as well as members of the Acidimicrobiia and Bacteroidia dominated the wood falls in the Eastern Mediterranean, while Alphaproteobacteria and Flavobacteriia colonized the Norwegian Sea wood logs. Fauna and bacterial wood-associated communities changed between 1 to 3 years of immersion, with sulphate-reducers and sulphide-oxidizers increasing in proportion, and putative cellulose degraders decreasing with time. Only 6% of all bacterial genera, comprising the core community, were found at any time

  14. Temporal and Spatial Variations of Bacterial and Faunal Communities Associated with Deep-Sea Wood Falls.

    Directory of Open Access Journals (Sweden)

    Petra Pop Ristova

    Full Text Available Sinking of large organic food falls i.e. kelp, wood and whale carcasses to the oligotrophic deep-sea floor promotes the establishment of locally highly productive and diverse ecosystems, often with specifically adapted benthic communities. However, the fragmented spatial distribution and small area poses challenges for the dispersal of their microbial and faunal communities. Our study focused on the temporal dynamics and spatial distributions of sunken wood bacterial communities, which were deployed in the vicinity of different cold seeps in the Eastern Mediterranean and the Norwegian deep-seas. By combining fingerprinting of bacterial communities by ARISA and 454 sequencing with in situ and ex situ biogeochemical measurements, we show that sunken wood logs have a locally confined long-term impact (> 3y on the sediment geochemistry and community structure. We confirm previous hypotheses of different successional stages in wood degradation including a sulphophilic one, attracting chemosynthetic fauna from nearby seep systems. Wood experiments deployed at similar water depths (1100-1700 m, but in hydrographically different oceanic regions harbored different wood-boring bivalves, opportunistic faunal communities, and chemosynthetic species. Similarly, bacterial communities on sunken wood logs were more similar within one geographic region than between different seas. Diverse sulphate-reducing bacteria of the Deltaproteobacteria, the sulphide-oxidizing bacteria Sulfurovum as well as members of the Acidimicrobiia and Bacteroidia dominated the wood falls in the Eastern Mediterranean, while Alphaproteobacteria and Flavobacteriia colonized the Norwegian Sea wood logs. Fauna and bacterial wood-associated communities changed between 1 to 3 years of immersion, with sulphate-reducers and sulphide-oxidizers increasing in proportion, and putative cellulose degraders decreasing with time. Only 6% of all bacterial genera, comprising the core community, were

  15. Bacterial Community Succession During in situ Uranium Bioremediation: Spatial Similarities Along Controlled Flow Paths

    International Nuclear Information System (INIS)

    Hwang, Chiachi; Wu, Weimin; Gentry, Terry J.; Carley, Jack; Corbin, Gail A.; Carroll, Sue L.; Watson, David B.; Jardine, Phil M.; Zhou, Jizhong; Criddle, Craig S.; Fields, Matthew W.

    2009-01-01

    Bacterial community succession was investigated in a field-scale subsurface reactor formed by a series of wells that received weekly ethanol additions to re-circulating groundwater. Ethanol additions stimulated denitrification, metal reduction, sulfate reduction, and U(VI) reduction to sparingly soluble U(IV). Clone libraries of SSU rRNA gene sequences from groundwater samples enabled tracking of spatial and temporal changes over a 1.5 y period. Analyses showed that the communities changed in a manner consistent with geochemical variations that occurred along temporal and spatial scales. Canonical correspondence analysis revealed that the levels of nitrate, uranium, sulfide, sulfate, and ethanol strongly correlated with particular bacterial populations. As sulfate and U(VI) levels declined, sequences representative of sulfate-reducers and metal-reducers were detected at high levels. Ultimately, sequences associated with sulfate-reducing populations predominated, and sulfate levels declined as U(VI) remained at low levels. When engineering controls were compared to the population variation via canonical ordination, changes could be related to dissolved oxygen control and ethanol addition. The data also indicated that the indigenous populations responded differently to stimulation for bio-reduction; however, the two bio-stimulated communities became more similar after different transitions in an idiosyncratic manner. The strong associations between particular environmental variables and certain populations provide insight into the establishment of practical and successful remediation strategies in radionuclide-contaminated environments with respect to engineering controls and microbial ecology.

  16. Bacterial Community Succession During in situ Uranium Bioremediation: Spatial Similarities Along Controlled Flow Paths

    Energy Technology Data Exchange (ETDEWEB)

    Hwang, Chiachi; Wu, Weimin; Gentry, Terry J.; Carley, Jack; Corbin, Gail A.; Carroll, Sue L.; Watson, David B.; Jardine, Phil M.; Zhou, Jizhong; Criddle, Craig S.; Fields, Matthew W.

    2009-05-22

    Bacterial community succession was investigated in a field-scale subsurface reactor formed by a series of wells that received weekly ethanol additions to re-circulating groundwater. Ethanol additions stimulated denitrification, metal reduction, sulfate reduction, and U(VI) reduction to sparingly soluble U(IV). Clone libraries of SSU rRNA gene sequences from groundwater samples enabled tracking of spatial and temporal changes over a 1.5 y period. Analyses showed that the communities changed in a manner consistent with geochemical variations that occurred along temporal and spatial scales. Canonical correspondence analysis revealed that the levels of nitrate, uranium, sulfide, sulfate, and ethanol strongly correlated with particular bacterial populations. As sulfate and U(VI) levels declined, sequences representative of sulfate-reducers and metal-reducers were detected at high levels. Ultimately, sequences associated with sulfate-reducing populations predominated, and sulfate levels declined as U(VI) remained at low levels. When engineering controls were compared to the population variation via canonical ordination, changes could be related to dissolved oxygen control and ethanol addition. The data also indicated that the indigenous populations responded differently to stimulation for bio-reduction; however, the two bio-stimulated communities became more similar after different transitions in an idiosyncratic manner. The strong associations between particular environmental variables and certain populations provide insight into the establishment of practical and successful remediation strategies in radionuclide-contaminated environments with respect to engineering controls and microbial ecology.

  17. Temporal and spatial influences incur reconfiguration of Arctic heathland soil bacterial community structure.

    Science.gov (United States)

    Hill, Richard; Saetnan, Eli R; Scullion, John; Gwynn-Jones, Dylan; Ostle, Nick; Edwards, Arwyn

    2016-06-01

    Microbial responses to Arctic climate change could radically alter the stability of major stores of soil carbon. However, the sensitivity of plot-scale experiments simulating climate change effects on Arctic heathland soils to potential confounding effects of spatial and temporal changes in soil microbial communities is unknown. Here, the variation in heathland soil bacterial communities at two survey sites in Sweden between spring and summer 2013 and at scales between 0-1 m and, 1-100 m and between sites (> 100 m) were investigated in parallel using 16S rRNA gene T-RFLP and amplicon sequencing. T-RFLP did not reveal spatial structuring of communities at scales structuring effects may not confound comparison between plot-scale treatments, temporal change is a significant influence. Moreover, the prominence of two temporally exclusive keystone taxa suggests that the stability of Arctic heathland soil bacterial communities could be disproportionally influenced by seasonal perturbations affecting individual taxa. © 2015 Society for Applied Microbiology and John Wiley & Sons Ltd.

  18. Spatially explicit models for inference about density in unmarked or partially marked populations

    Science.gov (United States)

    Chandler, Richard B.; Royle, J. Andrew

    2013-01-01

    Recently developed spatial capture–recapture (SCR) models represent a major advance over traditional capture–recapture (CR) models because they yield explicit estimates of animal density instead of population size within an unknown area. Furthermore, unlike nonspatial CR methods, SCR models account for heterogeneity in capture probability arising from the juxtaposition of animal activity centers and sample locations. Although the utility of SCR methods is gaining recognition, the requirement that all individuals can be uniquely identified excludes their use in many contexts. In this paper, we develop models for situations in which individual recognition is not possible, thereby allowing SCR concepts to be applied in studies of unmarked or partially marked populations. The data required for our model are spatially referenced counts made on one or more sample occasions at a collection of closely spaced sample units such that individuals can be encountered at multiple locations. Our approach includes a spatial point process for the animal activity centers and uses the spatial correlation in counts as information about the number and location of the activity centers. Camera-traps, hair snares, track plates, sound recordings, and even point counts can yield spatially correlated count data, and thus our model is widely applicable. A simulation study demonstrated that while the posterior mean exhibits frequentist bias on the order of 5–10% in small samples, the posterior mode is an accurate point estimator as long as adequate spatial correlation is present. Marking a subset of the population substantially increases posterior precision and is recommended whenever possible. We applied our model to avian point count data collected on an unmarked population of the northern parula (Parula americana) and obtained a density estimate (posterior mode) of 0.38 (95% CI: 0.19–1.64) birds/ha. Our paper challenges sampling and analytical conventions in ecology by demonstrating

  19. Inferring spatial clouds statistics from limited field-of-view, zenith observations

    Energy Technology Data Exchange (ETDEWEB)

    Sun, C.H.; Thorne, L.R. [Sandia National Labs., Livermore, CA (United States)

    1996-04-01

    Many of the Cloud and Radiation Testbed (CART) measurements produce a time series of zenith observations, but spatial averages are often the desired data product. One possible approach to deriving spatial averages from temporal averages is to invoke Taylor`s hypothesis where and when it is valid. Taylor`s hypothesis states that when the turbulence is small compared with the mean flow, the covariance in time is related to the covariance in space by the speed of the mean flow. For clouds fields, Taylor`s hypothesis would apply when the {open_quotes}local{close_quotes} turbulence is small compared with advective flow (mean wind). The objective of this study is to determine under what conditions Taylor`s hypothesis holds or does not hold true for broken cloud fields.

  20. Inferring spatial memory and spatiotemporal scaling from GPS data: comparing red deer Cervus elaphus movements with simulation models.

    Science.gov (United States)

    Gautestad, Arild O; Loe, Leif E; Mysterud, Atle

    2013-05-01

    1. Increased inference regarding underlying behavioural mechanisms of animal movement can be achieved by comparing GPS data with statistical mechanical movement models such as random walk and Lévy walk with known underlying behaviour and statistical properties. 2. GPS data are typically collected with ≥ 1 h intervals not exactly tracking every mechanistic step along the movement path, so a statistical mechanical model approach rather than a mechanistic approach is appropriate. However, comparisons require a coherent framework involving both scaling and memory aspects of the underlying process. Thus, simulation models have recently been extended to include memory-guided returns to previously visited patches, that is, site fidelity. 3. We define four main classes of movement, differing in incorporation of memory and scaling (based on respective intervals of the statistical fractal dimension D and presence/absence of site fidelity). Using three statistical protocols to estimate D and site fidelity, we compare these main movement classes with patterns observed in GPS data from 52 females of red deer (Cervus elaphus). 4. The results show best compliance with a scale-free and memory-enhanced kind of space use; that is, a power law distribution of step lengths, a fractal distribution of the spatial scatter of fixes and site fidelity. 5. Our study thus demonstrates how inference regarding memory effects and a hierarchical pattern of space use can be derived from analysis of GPS data. © 2013 The Authors. Journal of Animal Ecology © 2013 British Ecological Society.

  1. Penalized likelihood and multi-objective spatial scans for the detection and inference of irregular clusters

    Directory of Open Access Journals (Sweden)

    Fonseca Carlos M

    2010-10-01

    Full Text Available Abstract Background Irregularly shaped spatial clusters are difficult to delineate. A cluster found by an algorithm often spreads through large portions of the map, impacting its geographical meaning. Penalized likelihood methods for Kulldorff's spatial scan statistics have been used to control the excessive freedom of the shape of clusters. Penalty functions based on cluster geometry and non-connectivity have been proposed recently. Another approach involves the use of a multi-objective algorithm to maximize two objectives: the spatial scan statistics and the geometric penalty function. Results & Discussion We present a novel scan statistic algorithm employing a function based on the graph topology to penalize the presence of under-populated disconnection nodes in candidate clusters, the disconnection nodes cohesion function. A disconnection node is defined as a region within a cluster, such that its removal disconnects the cluster. By applying this function, the most geographically meaningful clusters are sifted through the immense set of possible irregularly shaped candidate cluster solutions. To evaluate the statistical significance of solutions for multi-objective scans, a statistical approach based on the concept of attainment function is used. In this paper we compared different penalized likelihoods employing the geometric and non-connectivity regularity functions and the novel disconnection nodes cohesion function. We also build multi-objective scans using those three functions and compare them with the previous penalized likelihood scans. An application is presented using comprehensive state-wide data for Chagas' disease in puerperal women in Minas Gerais state, Brazil. Conclusions We show that, compared to the other single-objective algorithms, multi-objective scans present better performance, regarding power, sensitivity and positive predicted value. The multi-objective non-connectivity scan is faster and better suited for the

  2. Inferring the flood frequency distribution for an ungauged basin using a spatially distributed rainfall-runoff model

    Directory of Open Access Journals (Sweden)

    G. Moretti

    2008-08-01

    Full Text Available The estimation of the peak river flow for ungauged river sections is a topical issue in applied hydrology. Spatially distributed rainfall-runoff models can be a useful tool to this end, since they are potentially able to simulate the river flow at any location of the watershed drainage network. However, it is not fully clear to what extent these models can provide reliable simulations over a wide range of spatial scales. This issue is investigated here by applying a spatially distributed, continuous simulation rainfall-runoff model to infer the flood frequency distribution of the Riarbero River. This is an ungauged mountain creek located in northern Italy, whose drainage area is 17 km2. The hydrological model is first calibrated by using a 1-year record of hourly meteorological data and river flows observed at the outlet of the 1294 km2 wide Secchia River basin, of which the Riarbero is a tributary. The model is then validated by performing a 100-year long simulation of synthetic river flow data, which allowed us to compare the simulated and observed flood frequency distributions at the Secchia River outlet and the internal cross river section of Cavola Bridge, where the basin area is 337 km2. Finally, another simulation of hourly river flows was performed by referring to the outlet of the Riarbero River, therefore allowing us to estimate the related flood frequency distribution. The results were validated by using estimates of peak river flow obtained by applying hydrological similarity principles and a regional method. The results show that the flood flow estimated through the application of the distributed model is consistent with the estimate provided by the regional procedure as well as the behaviors of the river banks. Conversely, the method based on hydrological similarity delivers an estimate that seems to be not as reliable. The analysis highlights interesting perspectives for the application of

  3. Penultimate modeling of spatial extremes: statistical inference for max-infinitely divisible processes

    KAUST Repository

    Huser, Raphaël

    2018-01-09

    Extreme-value theory for stochastic processes has motivated the statistical use of max-stable models for spatial extremes. However, fitting such asymptotic models to maxima observed over finite blocks is problematic when the asymptotic stability of the dependence does not prevail in finite samples. This issue is particularly serious when data are asymptotically independent, such that the dependence strength weakens and eventually vanishes as events become more extreme. We here aim to provide flexible sub-asymptotic models for spatially indexed block maxima, which more realistically account for discrepancies between data and asymptotic theory. We develop models pertaining to the wider class of max-infinitely divisible processes, extending the class of max-stable processes while retaining dependence properties that are natural for maxima: max-id models are positively associated, and they yield a self-consistent family of models for block maxima defined over any time unit. We propose two parametric construction principles for max-id models, emphasizing a point process-based generalized spectral representation, that allows for asymptotic independence while keeping the max-stable extremal-$t$ model as a special case. Parameter estimation is efficiently performed by pairwise likelihood, and we illustrate our new modeling framework with an application to Dutch wind gust maxima calculated over different time units.

  4. Hydroclimatology of Dual Peak Cholera Incidence in Bengal Region: Inferences from a Spatial Explicit Model

    Science.gov (United States)

    Bertuzzo, E.; Mari, L.; Righetto, L.; Casagrandi, R.; Gatto, M.; Rodriguez-Iturbe, I.; Rinaldo, A.

    2010-12-01

    The seasonality of cholera and its relation with environmental drivers are receiving increasing interest and research efforts, yet they remain unsatisfactorily understood. A striking example is the observed annual cycle of cholera incidence in the Bengal region which exhibits two peaks despite the main environmental drivers that have been linked to the disease (air and sea surface temperature, zooplankton density, river discharge) follow a synchronous single-peak annual pattern. A first outbreak, mainly affecting the coastal regions, occurs in spring and it is followed, after a period of low incidence during summer, by a second, usually larger, peak in autumn also involving regions situated farther inland. A hydroclimatological explanation for this unique seasonal cycle has been recently proposed: the low river spring flows favor the intrusion of brackish water (the natural environment of the causative agent of the disease) which, in turn, triggers the first outbreak. The summer rising river discharges have a temporary dilution effect and prompt the repulsion of contaminated water which lowers the disease incidence. However, the monsoon flooding, together with the induced crowding of the population and the failure of the sanitation systems, can possibly facilitate the spatial transmission of the disease and promote the autumn outbreak. We test this hypothesis using a mechanistic, spatially explicit model of cholera epidemic. The framework directly accounts for the role of the river network in transporting and redistributing cholera bacteria among human communities as well as for the annual fluctuation of the river flow. The model is forced with the actual environmental drivers of the region, namely river flow and temperature. Our results show that these two drivers, both having a single peak in the summer, can generate a double peak cholera incidence pattern. Besides temporal patterns, the model is also able to qualitatively reproduce spatial patterns characterized

  5. Temporal and spatial variabilities of Antarctic ice mass changes inferred by GRACE in a Bayesian framework

    Science.gov (United States)

    Wang, L.; Davis, J. L.; Tamisiea, M. E.

    2017-12-01

    The Antarctic ice sheet (AIS) holds about 60% of all fresh water on the Earth, an amount equivalent to about 58 m of sea-level rise. Observation of AIS mass change is thus essential in determining and predicting its contribution to sea level. While the ice mass loss estimates for West Antarctica (WA) and the Antarctic Peninsula (AP) are in good agreement, what the mass balance over East Antarctica (EA) is, and whether or not it compensates for the mass loss is under debate. Besides the different error sources and sensitivities of different measurement types, complex spatial and temporal variabilities would be another factor complicating the accurate estimation of the AIS mass balance. Therefore, a model that allows for variabilities in both melting rate and seasonal signals would seem appropriate in the estimation of present-day AIS melting. We present a stochastic filter technique, which enables the Bayesian separation of the systematic stripe noise and mass signal in decade-length GRACE monthly gravity series, and allows the estimation of time-variable seasonal and inter-annual components in the signals. One of the primary advantages of this Bayesian method is that it yields statistically rigorous uncertainty estimates reflecting the inherent spatial resolution of the data. By applying the stochastic filter to the decade-long GRACE observations, we present the temporal variabilities of the AIS mass balance at basin scale, particularly over East Antarctica, and decipher the EA mass variations in the past decade, and their role in affecting overall AIS mass balance and sea level.

  6. Spatial interpolation of hourly rainfall – effect of additional information, variogram inference and storm properties

    Directory of Open Access Journals (Sweden)

    A. Verworn

    2011-02-01

    Full Text Available Hydrological modelling of floods relies on precipitation data with a high resolution in space and time. A reliable spatial representation of short time step rainfall is often difficult to achieve due to a low network density. In this study hourly precipitation was spatially interpolated with the multivariate geostatistical method kriging with external drift (KED using additional information from topography, rainfall data from the denser daily networks and weather radar data. Investigations were carried out for several flood events in the time period between 2000 and 2005 caused by different meteorological conditions. The 125 km radius around the radar station Ummendorf in northern Germany covered the overall study region. One objective was to assess the effect of different approaches for estimation of semivariograms on the interpolation performance of short time step rainfall. Another objective was the refined application of the method kriging with external drift. Special attention was not only given to find the most relevant additional information, but also to combine the additional information in the best possible way. A multi-step interpolation procedure was applied to better consider sub-regions without rainfall.

    The impact of different semivariogram types on the interpolation performance was low. While it varied over the events, an averaged semivariogram was sufficient overall. Weather radar data were the most valuable additional information for KED for convective summer events. For interpolation of stratiform winter events using daily rainfall as additional information was sufficient. The application of the multi-step procedure significantly helped to improve the representation of fractional precipitation coverage.

  7. Similar processes but different environmental filters for soil bacterial and fungal community composition turnover on a broad spatial scale.

    Directory of Open Access Journals (Sweden)

    Nicolas Chemidlin Prévost-Bouré

    Full Text Available Spatial scaling of microorganisms has been demonstrated over the last decade. However, the processes and environmental filters shaping soil microbial community structure on a broad spatial scale still need to be refined and ranked. Here, we compared bacterial and fungal community composition turnovers through a biogeographical approach on the same soil sampling design at a broad spatial scale (area range: 13300 to 31000 km2: i to examine their spatial structuring; ii to investigate the relative importance of environmental selection and spatial autocorrelation in determining their community composition turnover; and iii to identify and rank the relevant environmental filters and scales involved in their spatial variations. Molecular fingerprinting of soil bacterial and fungal communities was performed on 413 soils from four French regions of contrasting environmental heterogeneity (LandesBacterial and fungal community composition turnovers were mainly driven by environmental selection explaining from 10% to 20% of community composition variations, but spatial variables also explained 3% to 9% of total variance. These variables highlighted significant spatial autocorrelation of both communities unexplained by the environmental variables measured and could partly be explained by dispersal limitations. Although the identified filters and their hierarchy were dependent on the region and organism, selection was systematically based on a common group of environmental variables: pH, trophic resources, texture and land use. Spatial autocorrelation was also important at

  8. Similar processes but different environmental filters for soil bacterial and fungal community composition turnover on a broad spatial scale.

    Science.gov (United States)

    Chemidlin Prévost-Bouré, Nicolas; Dequiedt, Samuel; Thioulouse, Jean; Lelièvre, Mélanie; Saby, Nicolas P A; Jolivet, Claudy; Arrouays, Dominique; Plassart, Pierre; Lemanceau, Philippe; Ranjard, Lionel

    2014-01-01

    Spatial scaling of microorganisms has been demonstrated over the last decade. However, the processes and environmental filters shaping soil microbial community structure on a broad spatial scale still need to be refined and ranked. Here, we compared bacterial and fungal community composition turnovers through a biogeographical approach on the same soil sampling design at a broad spatial scale (area range: 13300 to 31000 km2): i) to examine their spatial structuring; ii) to investigate the relative importance of environmental selection and spatial autocorrelation in determining their community composition turnover; and iii) to identify and rank the relevant environmental filters and scales involved in their spatial variations. Molecular fingerprinting of soil bacterial and fungal communities was performed on 413 soils from four French regions of contrasting environmental heterogeneity (Landescommunities' composition turnovers. The relative importance of processes and filters was assessed by distance-based redundancy analysis. This study demonstrates significant community composition turnover rates for soil bacteria and fungi, which were dependent on the region. Bacterial and fungal community composition turnovers were mainly driven by environmental selection explaining from 10% to 20% of community composition variations, but spatial variables also explained 3% to 9% of total variance. These variables highlighted significant spatial autocorrelation of both communities unexplained by the environmental variables measured and could partly be explained by dispersal limitations. Although the identified filters and their hierarchy were dependent on the region and organism, selection was systematically based on a common group of environmental variables: pH, trophic resources, texture and land use. Spatial autocorrelation was also important at coarse (80 to 120 km radius) and/or medium (40 to 65 km radius) spatial scales, suggesting dispersal limitations at these scales.

  9. The Composition and Spatial Patterns of Bacterial Virulence Factors and Antibiotic Resistance Genes in 19 Wastewater Treatment Plants.

    Directory of Open Access Journals (Sweden)

    Bing Zhang

    Full Text Available Bacterial pathogenicity and antibiotic resistance are of concern for environmental safety and public health. Accumulating evidence suggests that wastewater treatment plants (WWTPs are as an important sink and source of pathogens and antibiotic resistance genes (ARGs. Virulence genes (encoding virulence factors are good indicators for bacterial pathogenic potentials. To achieve a comprehensive understanding of bacterial pathogenic potentials and antibiotic resistance in WWTPs, bacterial virulence genes and ARGs in 19 WWTPs covering a majority of latitudinal zones of China were surveyed by using GeoChip 4.2. A total of 1610 genes covering 13 virulence factors and 1903 genes belonging to 11 ARG families were detected respectively. The bacterial virulence genes exhibited significant spatial distribution patterns of a latitudinal biodiversity gradient and a distance-decay relationship across China. Moreover, virulence genes tended to coexist with ARGs as shown by their strongly positive associations. In addition, key environmental factors shaping the overall virulence gene structure were identified. This study profiles the occurrence, composition and distribution of virulence genes and ARGs in current WWTPs in China, and uncovers spatial patterns and important environmental variables shaping their structure, which may provide the basis for further studies of bacterial virulence factors and antibiotic resistance in WWTPs.

  10. Spatial patterns, ecological niches, and interspecific competition of avian brood parasites: inferring from a case study of Korea.

    Science.gov (United States)

    Lee, Jin-Won; Noh, Hee-Jin; Lee, Yunkyoung; Kwon, Young-Soo; Kim, Chang-Hoe; Yoo, Jeong-Chil

    2014-09-01

    Since obligate avian brood parasites depend completely on the effort of other host species for rearing their progeny, the availability of hosts will be a critical resource for their life history. Circumstantial evidence suggests that intense competition for host species may exist not only within but also between species. So far, however, few studies have demonstrated whether the interspecific competition really occurs in the system of avian brood parasitism and how the nature of brood parasitism is related to their niche evolution. Using the occurrence data of five avian brood parasites from two sources of nationwide bird surveys in South Korea and publically available environmental/climatic data, we identified their distribution patterns and ecological niches, and applied species distribution modeling to infer the effect of interspecific competition on their spatial distribution. We found that the distribution patterns of five avian brood parasites could be characterized by altitude and climatic conditions, but overall their spatial ranges and ecological niches extensively overlapped with each other. We also found that the predicted distribution areas of each species were generally comparable to the realized distribution areas, and the numbers of individuals in areas where multiple species were predicted to coexist showed positive relationships among species. In conclusion, despite following different coevolutionary trajectories to adapt to their respect host species, five species of avian brood parasites breeding in South Korea occupied broadly similar ecological niches, implying that they tend to conserve ancestral preferences for ecological conditions. Furthermore, our results indicated that contrary to expectation interspecific competition for host availability between avian brood parasites seemed to be trivial, and thus, play little role in shaping their spatial distributions and ecological niches. Future studies, including the complete ranges of avian brood

  11. A framework for inference about carnivore density from unstructured spatial sampling of scat using detector dogs

    Science.gov (United States)

    Thompson, Craig M.; Royle, J. Andrew; Garner, James D.

    2012-01-01

    Wildlife management often hinges upon an accurate assessment of population density. Although undeniably useful, many of the traditional approaches to density estimation such as visual counts, livetrapping, or mark–recapture suffer from a suite of methodological and analytical weaknesses. Rare, secretive, or highly mobile species exacerbate these problems through the reality of small sample sizes and movement on and off study sites. In response to these difficulties, there is growing interest in the use of non-invasive survey techniques, which provide the opportunity to collect larger samples with minimal increases in effort, as well as the application of analytical frameworks that are not reliant on large sample size arguments. One promising survey technique, the use of scat detecting dogs, offers a greatly enhanced probability of detection while at the same time generating new difficulties with respect to non-standard survey routes, variable search intensity, and the lack of a fixed survey point for characterizing non-detection. In order to account for these issues, we modified an existing spatially explicit, capture–recapture model for camera trap data to account for variable search intensity and the lack of fixed, georeferenced trap locations. We applied this modified model to a fisher (Martes pennanti) dataset from the Sierra National Forest, California, and compared the results (12.3 fishers/100 km2) to more traditional density estimates. We then evaluated model performance using simulations at 3 levels of population density. Simulation results indicated that estimates based on the posterior mode were relatively unbiased. We believe that this approach provides a flexible analytical framework for reconciling the inconsistencies between detector dog survey data and density estimation procedures.

  12. Inferring Growth Control Mechanisms in Growing Multi-cellular Spheroids of NSCLC Cells from Spatial-Temporal Image Data.

    Science.gov (United States)

    Jagiella, Nick; Müller, Benedikt; Müller, Margareta; Vignon-Clementel, Irene E; Drasdo, Dirk

    2016-02-01

    We develop a quantitative single cell-based mathematical model for multi-cellular tumor spheroids (MCTS) of SK-MES-1 cells, a non-small cell lung cancer (NSCLC) cell line, growing under various nutrient conditions: we confront the simulations performed with this model with data on the growth kinetics and spatial labeling patterns for cell proliferation, extracellular matrix (ECM), cell distribution and cell death. We start with a simple model capturing part of the experimental observations. We then show, by performing a sensitivity analysis at each development stage of the model that its complexity needs to be stepwise increased to account for further experimental growth conditions. We thus ultimately arrive at a model that mimics the MCTS growth under multiple conditions to a great extent. Interestingly, the final model, is a minimal model capable of explaining all data simultaneously in the sense, that the number of mechanisms it contains is sufficient to explain the data and missing out any of its mechanisms did not permit fit between all data and the model within physiological parameter ranges. Nevertheless, compared to earlier models it is quite complex i.e., it includes a wide range of mechanisms discussed in biological literature. In this model, the cells lacking oxygen switch from aerobe to anaerobe glycolysis and produce lactate. Too high concentrations of lactate or too low concentrations of ATP promote cell death. Only if the extracellular matrix density overcomes a certain threshold, cells are able to enter the cell cycle. Dying cells produce a diffusive growth inhibitor. Missing out the spatial information would not permit to infer the mechanisms at work. Our findings suggest that this iterative data integration together with intermediate model sensitivity analysis at each model development stage, provide a promising strategy to infer predictive yet minimal (in the above sense) quantitative models of tumor growth, as prospectively of other tissue

  13. Ensemble Kalman Filter Inference of Spatially-varying Manning’s n coefficients in the Coastal Ocean

    KAUST Repository

    Siripatana, Adil

    2018-05-16

    Ensemble Kalman (EnKF) filtering is an established framework for large scale state estimation problems. EnKFs can also be used for state-parameter estimation, using the so-called “Joint-EnKF” approach. The idea is simply to augment the state vector with the parameters to be estimated and assign invariant dynamics for the time evolution of the parameters. In this contribution, we investigate the efficiency of the Joint-EnKF for estimating spatially-varying Manning’s n coefficients used to define the bottom roughness in the Shallow Water Equations (SWEs) of a coastal ocean model.Observation System Simulation Experiments (OSSEs) are conducted using the ADvanced CIRCulation (ADCIRC) model, which solves a modified form of the Shallow Water Equations. A deterministic EnKF, the Singular Evolutive Interpolated Kalman (SEIK) filter, is used to estimate a vector of Manning’s n coefficients defined at the model nodal points by assimilating synthetic water elevation data. It is found that with reasonable ensemble size (O(10)), the filter’s estimate converges to the reference Manning’s field. To enhance performance, we have further reduced the dimension of the parameter search space through a Karhunen-Loéve (KL) expansion. We have also iterated on the filter update step to better account for the nonlinearity of the parameter estimation problem. We study the sensitivity of the system to the ensemble size, localization scale, dimension of retained KL modes, and number of iterations. The performance of the proposed framework in term of estimation accuracy suggests that a well-tuned Joint-EnKF provides a promising robust approach to infer spatially varying seabed roughness parameters in the context of coastal ocean modeling.

  14. Process Inference from High Frequency Temporal Variations in Dissolved Organic Carbon (DOC) Dynamics Across Nested Spatial Scales

    Science.gov (United States)

    Tunaley, C.; Tetzlaff, D.; Lessels, J. S.; Soulsby, C.

    2014-12-01

    In order to understand aquatic ecosystem functioning it is critical to understand the processes that control the spatial and temporal variations in DOC. DOC concentrations are highly dynamic, however, our understanding at short, high frequency timescales is still limited. Optical sensors which act as a proxy for DOC provide the opportunity to investigate near-continuous DOC variations in order to understand the hydrological and biogeochemical processes that control concentrations at short temporal scales. Here we present inferred 15 minute stream water DOC data for a 12 month period at three nested scales (1km2, 3km2 and 31km2) for the Bruntland Burn, a headwater catchment in NE Scotland. High frequency data were measured using FDOM and CDOM probes which work by measuring the fluorescent component and coloured component, respectively, of DOC when exposed to ultraviolet light. Both FDOM and CDOM were strongly correlated (r2 >0.8) with DOC allowing high frequency estimations. Results show the close coupling of DOC with discharge throughout the sampling period at all three spatial scales. However, analysis at the event scale highlights anticlockwise hysteresis relationships between DOC and discharge due to the delay in DOC being flushed from the increasingly large areas of peaty soils as saturation zones expand and increase hydrological connectivity. Lag times vary between events dependent on antecedent conditions. During a 10 year drought period in late summer 2013 it was apparent that very small changes in discharge on a 15 minute timescale result in high increases in DOC. This suggests transport limitation during this period where DOC builds up in the soil and is not flushed regularly, therefore any subsequent increase in discharge results in large DOC peaks. The high frequency sensors also reveal diurnal variability during summer months related to the photo-oxidation, evaporative and biological influences of DOC during the day. This relationship is less

  15. CMEIAS-Aided Microscopy of the Spatial Ecology of Individual Bacterial Interactions Involving Cell-to-Cell Communication within Biofilms

    Directory of Open Access Journals (Sweden)

    Frank B. Dazzo

    2012-05-01

    Full Text Available This paper describes how the quantitative analytical tools of CMEIAS image analysis software can be used to investigate in situ microbial interactions involving cell-to-cell communication within biofilms. Various spatial pattern analyses applied to the data extracted from the 2-dimensional coordinate positioning of individual bacterial cells at single-cell resolution indicate that microbial colonization within natural biofilms is not a spatially random process, but rather involves strong positive interactions between communicating cells that influence their neighbors’ aggregated colonization behavior. Geostatistical analysis of the data provide statistically defendable estimates of the micrometer scale and interpolation maps of the spatial heterogeneity and local intensity at which these microbial interactions autocorrelate with their spatial patterns of distribution. Including in situ image analysis in cell communication studies fills an important gap in understanding the spatially dependent microbial ecophysiology that governs the intensity of biofilm colonization and its unique architecture.

  16. Analysis on the Spatial Difference of Bacterial Community Structure in Micro-pressure Air-lift Loop Reactor

    Science.gov (United States)

    Wan, L. G.; Lin, Q.; Bian, D. J.; Ren, Q. K.; Xiao, Y. B.; Lu, W. X.

    2018-02-01

    In order to reveal the spatial difference of the bacterial community structure in the Micro-pressure Air-lift Loop Reactor, the activated sludge bacterial at five different representative sites in the reactor were studied by denaturing gradient gel electrophoresis (DGGE). The results of DGGE showed that the difference of environmental conditions (such as substrate concentration, dissolved oxygen and PH, etc.) resulted in different diversity and similarity of microbial flora in different spatial locations. The Shannon-Wiener diversity index of the total bacterial samples from five sludge samples varied from 0.92 to 1.28, the biodiversity index was the smallest at point 5, and the biodiversity index was the highest at point 2. The similarity of the flora between the point 2, 3 and 4 was 80% or more, respectively. The similarity of the flora between the point 5 and the other samples was below 70%, and the similarity of point 2 was only 59.2%. Due to the different contribution of different strains to the removal of pollutants, it can give full play to the synergistic effect of bacterial degradation of pollutants, and further improve the efficiency of sewage treatment.

  17. Spatial and temporal changes in sulphate-reducing groundwater bacterial community structure in response to Managed Aquifer Recharge.

    Science.gov (United States)

    Reed, D A; Toze, S; Chang, B

    2008-01-01

    The population dynamics of bacterial able to be cultured under sulphate reducing condition was studied in conjunction with changes in aquifer geochemistry using multivariate statistics for two contrasting Managed Aquifer Recharge (MAR) techniques at two different geographical locations (Perth, Western Australia and Adelaide, South Australia). Principal component analysis (PCA) was used to investigate spatial and temporal changes in the overall chemical signature of the aquifers using an array of chemical analytes which demonstrated a migrating geochemical plume. Denaturing Gradient Gel Electrophoresis (DGGE) using DNA from sulphate-reducing bacteria cultures was used to detect spatial and temporal changes in population dynamics. Bacterial and geochemical evidence suggested that groundwater at greatest distance from the nutrient source was least affected by treated effluent recharge. The results suggested that bacterial populations that were able to be cultured in sulphate reducing media responded to the migrating chemical gradient and to the changes in aquifer geochemistry. Most noticeably, sulphate-reducing bacterial populations associated with the infiltration galleries were stable in community structure over time. Additionally, the biodiversity of these culturable bacteria was restored when aquifer geochemistry returned to ambient conditions during the recovery phase at the Adelaide Aquifer Storage and Recovery site. Copyright CSIRO 2008.

  18. Spatial Distribution of Bacterial Communities Driven by Multiple Environmental Factors in a Beach Wetland of the Largest Freshwater Lake in China

    Directory of Open Access Journals (Sweden)

    Xia eDing

    2015-02-01

    Full Text Available The spatial distributions of bacterial communities may be driven by multiple environmental factors. Thus, understanding the relationships between bacterial distribution and environmental factors is critical for understanding wetland stability and the functioning of freshwater lakes. However, little research on the bacterial communities in deep sediment layers exists. In this study, thirty clone libraries of 16S rRNA were constructed from a beach wetland of the Poyang Lake along both horizontal (distance to the water-land junction and vertical (sediment depth gradients to assess the effects of sediment properties on bacterial community structure and diversity. Our results showed that bacterial diversity increased along the horizontal gradient and decreased along the vertical gradient. The heterogeneous sediment properties along gradients substantially affected the dominant bacterial groups at the phylum and species levels. For example, the NH4+ concentration decreased with increasing depth, which was positively correlated with the relative abundance of Alphaproteobacteria. The changes in bacterial diversity and dominant bacterial groups showed that the top layer had a different bacterial community structure than the deeper layers. Principal component analysis revealed that both gradients, not each gradient independently, contributed to the shift in the bacterial community structure. A multiple linear regression model explained the changes in bacterial diversity and richness along the depth and distance gradients. Overall, our results suggest that spatial gradients associated with sediment properties shaped the bacterial communities in the Poyang Lake beach wetland.

  19. Spatial and temporal occurrence of bacterial pathogens in rural water supplies, Southern Alberta, Canada

    Science.gov (United States)

    Gannon, V.; Graham, T. A.; Read, S.; Ziebell, K.; Muckle, A.; Thomas, J.; Selinger, B.; Kienzle, S.; Lapp, S. L.; Townshend, I.; Byrne, J.

    2002-12-01

    Southern Alberta has the highest rate of gastrointestinal illness in the province, and some of the highest infection rates in Canada. The region has extensive field crop irrigation system supporting a rapidly expanding animal agriculture industry. Recently, there has been much public concern about the safety and quality of water in this region, particularly with respect to drinking water supplies for farm residences and rural communities, where water treatment may be less than optimal. We have tested raw river and irrigation water in the Oldman River Basin in southern Alberta for the presence of bacterial pathogens (E. coli O157:H7 and Salmonella spp ) as well as made counts of total and faecal coliforms over the last two years (2000-2001). E. coli O157:H7 and Salmonella spp. isolations and coliform counts peak in raw water from this system during the summer months. E. coli O157:H7 was only isolated from 27/1624 (1.7%) and Salmonella was isolated from 158/1624 (9.7%) of raw water samples over the two year period. Certain sites had multiple pathogen isolations and high indicator bacteria counts in the same year and from year to year. Certain sites had multiple pathogen isolations and high indicator bacteria counts in the same year and from year to year. S. Rublislaw was the most common Salmonella serovar isolated in both years. While this serovar is rarely associated with human or animal disease in Alberta, other Salmonella serovars isolated, such as Typhimurium, are commonly disease-associated. This poster presents initial analyses of the spatial and temporal properties of pathogen occurrences in the Oldman Basin in 2000 and 2001. Seasonal variability in the occurrence of pathogens is particularly interesting and of concern. Early results demonstrate the pathogen occurrences peak during the height of the summer recreation season; posing a substantial infection risk for the public and tourism populations. Human consumption of inadequately treated water in this

  20. Temporal and Spatial Impact of Human Cadaver Decomposition on Soil Bacterial and Arthropod Community Structure and Function

    Science.gov (United States)

    Singh, Baneshwar; Minick, Kevan J.; Strickland, Michael S.; Wickings, Kyle G.; Crippen, Tawni L.; Tarone, Aaron M.; Benbow, M. Eric; Sufrin, Ness; Tomberlin, Jeffery K.; Pechal, Jennifer L.

    2018-01-01

    As vertebrate carrion decomposes, there is a release of nutrient-rich fluids into the underlying soil, which can impact associated biological community structure and function. How these changes alter soil biogeochemical cycles is relatively unknown and may prove useful in the identification of carrion decomposition islands that have long lasting, focal ecological effects. This study investigated the spatial (0, 1, and 5 m) and temporal (3–732 days) dynamics of human cadaver decomposition on soil bacterial and arthropod community structure and microbial function. We observed strong evidence of a predictable response to cadaver decomposition that varies over space for soil bacterial and arthropod community structure, carbon (C) mineralization and microbial substrate utilization patterns. In the presence of a cadaver (i.e., 0 m samples), the relative abundance of Bacteroidetes and Firmicutes was greater, while the relative abundance of Acidobacteria, Chloroflexi, Gemmatimonadetes, and Verrucomicrobia was lower when compared to samples at 1 and 5 m. Micro-arthropods were more abundant (15 to 17-fold) in soils collected at 0 m compared to either 1 or 5 m, but overall, micro-arthropod community composition was unrelated to either bacterial community composition or function. Bacterial community structure and microbial function also exhibited temporal relationships, whereas arthropod community structure did not. Cumulative precipitation was more effective in predicting temporal variations in bacterial abundance and microbial activity than accumulated degree days. In the presence of the cadaver (i.e., 0 m samples), the relative abundance of Actinobacteria increased significantly with cumulative precipitation. Furthermore, soil bacterial communities and C mineralization were sensitive to the introduction of human cadavers as they diverged from baseline levels and did not recover completely in approximately 2 years. These data are valuable for understanding ecosystem

  1. Temporal and Spatial Impact of Human Cadaver Decomposition on Soil Bacterial and Arthropod Community Structure and Function

    Directory of Open Access Journals (Sweden)

    Baneshwar Singh

    2018-01-01

    Full Text Available As vertebrate carrion decomposes, there is a release of nutrient-rich fluids into the underlying soil, which can impact associated biological community structure and function. How these changes alter soil biogeochemical cycles is relatively unknown and may prove useful in the identification of carrion decomposition islands that have long lasting, focal ecological effects. This study investigated the spatial (0, 1, and 5 m and temporal (3–732 days dynamics of human cadaver decomposition on soil bacterial and arthropod community structure and microbial function. We observed strong evidence of a predictable response to cadaver decomposition that varies over space for soil bacterial and arthropod community structure, carbon (C mineralization and microbial substrate utilization patterns. In the presence of a cadaver (i.e., 0 m samples, the relative abundance of Bacteroidetes and Firmicutes was greater, while the relative abundance of Acidobacteria, Chloroflexi, Gemmatimonadetes, and Verrucomicrobia was lower when compared to samples at 1 and 5 m. Micro-arthropods were more abundant (15 to 17-fold in soils collected at 0 m compared to either 1 or 5 m, but overall, micro-arthropod community composition was unrelated to either bacterial community composition or function. Bacterial community structure and microbial function also exhibited temporal relationships, whereas arthropod community structure did not. Cumulative precipitation was more effective in predicting temporal variations in bacterial abundance and microbial activity than accumulated degree days. In the presence of the cadaver (i.e., 0 m samples, the relative abundance of Actinobacteria increased significantly with cumulative precipitation. Furthermore, soil bacterial communities and C mineralization were sensitive to the introduction of human cadavers as they diverged from baseline levels and did not recover completely in approximately 2 years. These data are valuable for understanding

  2. Coupling Spatial Segregation with Synthetic Circuits to Control Bacterial Survival (Open Access)

    Science.gov (United States)

    2016-02-29

    gated by encapsulation ( APA membrane), we developed a full model, specific for the BlaM circuit to account for the essential reac- tions involved in...L-lysine)-alginate ( APA ) microcapsules containing bacterial cells were fabricated by a soft lithography technique (Duffy et al, 1998). The...HB, Kolter R (2000) BIOFILM FORMATION AS MICROBIAL DEVELOPMENT. Annu Rev Microbiol 54: 49 – 79 Pai A, You L (2009) Optimal tuning of bacterial sensing

  3. Phage-Bacterial Dynamics with Spatial Structure: Self Organization around Phage Sinks Can Promote Increased Cell Densities.

    Science.gov (United States)

    Bull, James J; Christensen, Kelly A; Scott, Carly; Jack, Benjamin R; Crandall, Cameron J; Krone, Stephen M

    2018-01-29

    Bacteria growing on surfaces appear to be profoundly more resistant to control by lytic bacteriophages than do the same cells grown in liquid. Here, we use simulation models to investigate whether spatial structure per se can account for this increased cell density in the presence of phages. A measure is derived for comparing cell densities between growth in spatially structured environments versus well mixed environments (known as mass action). Maintenance of sensitive cells requires some form of phage death; we invoke death mechanisms that are spatially fixed, as if produced by cells. Spatially structured phage death provides cells with a means of protection that can boost cell densities an order of magnitude above that attained under mass action, although the effect is sometimes in the opposite direction. Phage and bacteria self organize into separate refuges, and spatial structure operates so that the phage progeny from a single burst do not have independent fates (as they do with mass action). Phage incur a high loss when invading protected areas that have high cell densities, resulting in greater protection for the cells. By the same metric, mass action dynamics either show no sustained bacterial elevation or oscillate between states of low and high cell densities and an elevated average. The elevated cell densities observed in models with spatial structure do not approach the empirically observed increased density of cells in structured environments with phages (which can be many orders of magnitude), so the empirical phenomenon likely requires additional mechanisms than those analyzed here.

  4. Effect of bacterial ice nuclei on the frequency and intensity of lightning activity inferred by the BRAMS model

    Directory of Open Access Journals (Sweden)

    F. L. T. Gonçalves

    2012-07-01

    , are from 3.1 to 3.7 higher than the BRAMS default. Even the lower bacterial concentrations (scenarios S2 and S3 produced 3 time higher number of flashes, compared to S5 scenario. This result is a function of the hydrometeors in each simulation. In conclusion, IN bacteria could affect directly the thunderstorm structure and lightning formation with many other microphysical implications.

  5. Spatial distribution of archaeal and bacterial ammonia oxidizers in the littoral buffer zone of a nitrogen-rich lake.

    Science.gov (United States)

    Wang, Yu; Zhu, Guibing; Ye, Lei; Feng, Xiaojuan; Op den Camp, Huub J M; Yin, Chengqing

    2012-01-01

    The spatial distribution and diversity of archaeal and bacterial ammonia oxidizers (AOA and AOB) were evaluated targeting amoA genes in the gradient of a littoral buffer zone which has been identified as a hot spot for N cycling. Here we found high spatial heterogeneity in the nitrification rate and abundance of ammonia oxidizers in the five sampling sites. The bacterial amoA gene was numerically dominant in most of the surface soil but decreased dramatically in deep layers. Higher nitrification potentials were detected in two sites near the land/water interface at 4.4-6.1 microg NO(2-)-N/(g dry weight soil x hr), while only 1.0-1.7 microg NO(2-)-N/(g dry weight soil x hr) was measured at other sites. The potential nitrification rates were proportional to the amoA gene abundance for AOB, but with no significant correlation with AOA. The NH4+ concentration was the most determinative parameter for the abundance of AOB and potential nitrification rates in this study. Higher richness in the surface layer was found in the analysis of biodiversity. Phylogenetic analysis revealed that most of the bacterial amoA sequences in surface soil were affiliated with the genus of Nitrosopira while the archaeal sequences were almost equally affiliated with Candidatus 'Nitrososphaera gargensis' and Candidatus 'Nitrosocaldus yellowstonii'. The spatial distribution of AOA and AOB indicated that bacteria may play a more important role in nitrification in the littoral buffer zone of a N-rich lake.

  6. The effects of host age and spatial location on bacterial community composition in the English Oak tree (Quercus robur).

    Science.gov (United States)

    Meaden, S; Metcalf, C J E; Koskella, B

    2016-04-27

    Drivers of bacterial community assemblages associated with plants are diverse and include biotic factors, such as competitors and host traits, and abiotic factors, including environmental conditions and dispersal mechanisms. We examine the roles of spatial distribution and host size, as an approximation for age, in shaping the microbiome associated with Quercus robur woody tissue using culture-independent 16S rRNA gene amplicon sequencing. In addition to providing a baseline survey of the Q. robur microbiome, we screened for the pathogen of acute oak decline. Our results suggest that age is a predictor of bacterial community composition, demonstrating a surprising negative correlation between tree age and alpha diversity. We find no signature of dispersal limitation within the Wytham Woods plot sampled. Together, these results provide evidence for niche-based hypotheses of community assembly and the importance of tree age in bacterial community structure, as well as highlighting that caution must be applied when diagnosing dysbiosis in a long-lived plant host. © 2016 The Authors. Environmental Microbiology published by Society for Applied Microbiology and John Wiley & Sons Ltd.

  7. Spatial and Temporal Relationships Between Localized Corrosion and Bacterial Activtty on lron-Containing Substrata

    National Research Council Canada - National Science Library

    Franklin, M

    1999-01-01

    A series of laboratory and field experiments were designed to determine the temporal and spatial relationships between accumulations of bacteria and pitting corrosion of iron-containing metals exposed...

  8. Spatial variation in deposition rate coefficients of an adhesion-deficient bacterial strain in quartz sand.

    Science.gov (United States)

    Tong, Meiping; Camesano, Terri A; Johnson, William P

    2005-05-15

    The transport of bacterial strain DA001 was examined in packed quartz sand under a variety of environmentally relevant ionic strength and flow conditions. Under all conditions, the retained bacterial concentrations decreased with distance from the column inlet at a rate that was faster than loglinear, indicating that the deposition rate coefficient decreased with increasing transport distance. The hyperexponential retained profile contrasted againstthe nonmonotonic retained profiles that had been previously observed for this same bacterial strain in glass bead porous media, demonstrating that the form of deviation from log-linear behavior is highly sensitive to system conditions. The deposition rate constants in quartz sand were orders of magnitude below those expected from filtration theory, even in the absence of electrostatic energy barriers. The degree of hyperexponential deviation of the retained profiles from loglinear behavior did not decrease with increasing ionic strength in quartz sand. These observations demonstrate thatthe observed low adhesion and deviation from log-linear behavior was not driven by electrostatic repulsion. Measurements of the interaction forces between DA001 cells and the silicon nitride tip of an atomic force microscope (AFM) showed that the bacterium possesses surface polymers with an average equilibrium length of 59.8 nm. AFM adhesion force measurements revealed low adhesion affinities between silicon nitride and DA001 polymers with approximately 95% of adhesion forces having magnitudes responsible for the low adhesion to silicon nitride, indicating that steric interactions from extracellular polymers controlled DA001 adhesion deficiency and deviation from log-linear behavior on quartz sand.

  9. Preschool children use space, rather than counting, to infer the numerical magnitude of digits: Evidence for a spatial mapping principle.

    Science.gov (United States)

    Sella, Francesco; Berteletti, Ilaria; Lucangeli, Daniela; Zorzi, Marco

    2017-01-01

    A milestone in numerical development is the acquisition of counting principles which allow children to exactly determine the numerosity of a given set. Moreover, a canonical left-to-right spatial layout for representing numbers also emerges during preschool. These foundational aspects of numerical competence have been extensively studied, but there is sparse knowledge about the interplay between the acquisition of the cardinality principle and spatial mapping of numbers in early numerical development. The present study investigated how these skills concurrently develop before formal schooling. Preschool children were classified according to their performance in Give-a-Number and Number-to-position tasks. Experiment 1 revealed three qualitatively different groups: (i) children who did not master the cardinality principle and lacked any consistent spatial mapping for digits, (ii) children who mastered the cardinality principle and yet failed in spatial mapping, and (iii) children who mastered the cardinality principle and displayed consistent spatial mapping. This suggests that mastery of the cardinality principle does not entail the emergence of spatial mapping. Experiment 2 confirmed the presence of these three developmental stages and investigated their relation with a digit comparison task. Crucially, only children who displayed a consistent spatial mapping of numbers showed the ability to compare digits by numerical magnitude. A congruent (i.e., numerically ordered) positioning of numbers onto a visual line as well as the concept that moving rightwards (in Western cultures) conveys an increase in numerical magnitude mark the mastery of a spatial mapping principle. Children seem to rely on this spatial organization to achieve a full understanding of the magnitude relations between digits. Copyright © 2016 Elsevier B.V. All rights reserved.

  10. Slip rates and spatially variable creep on faults of the northern San Andreas system inferred through Bayesian inversion of Global Positioning System data

    Science.gov (United States)

    Murray, Jessica R.; Minson, Sarah E.; Svarc, Jerry L.

    2014-01-01

    Fault creep, depending on its rate and spatial extent, is thought to reduce earthquake hazard by releasing tectonic strain aseismically. We use Bayesian inversion and a newly expanded GPS data set to infer the deep slip rates below assigned locking depths on the San Andreas, Maacama, and Bartlett Springs Faults of Northern California and, for the latter two, the spatially variable interseismic creep rate above the locking depth. We estimate deep slip rates of 21.5 ± 0.5, 13.1 ± 0.8, and 7.5 ± 0.7 mm/yr below 16 km, 9 km, and 13 km on the San Andreas, Maacama, and Bartlett Springs Faults, respectively. We infer that on average the Bartlett Springs fault creeps from the Earth's surface to 13 km depth, and below 5 km the creep rate approaches the deep slip rate. This implies that microseismicity may extend below the locking depth; however, we cannot rule out the presence of locked patches in the seismogenic zone that could generate moderate earthquakes. Our estimated Maacama creep rate, while comparable to the inferred deep slip rate at the Earth's surface, decreases with depth, implying a slip deficit exists. The Maacama deep slip rate estimate, 13.1 mm/yr, exceeds long-term geologic slip rate estimates, perhaps due to distributed off-fault strain or the presence of multiple active fault strands. While our creep rate estimates are relatively insensitive to choice of model locking depth, insufficient independent information regarding locking depths is a source of epistemic uncertainty that impacts deep slip rate estimates.

  11. Spatial Homogeneity of Bacterial Communities Associated with the Surface Mucus Layer of the Reef-Building Coral Acropora palmata.

    Directory of Open Access Journals (Sweden)

    Dustin W Kemp

    Full Text Available Coral surface mucus layer (SML microbiota are critical components of the coral holobiont and play important roles in nutrient cycling and defense against pathogens. We sequenced 16S rRNA amplicons to examine the structure of the SML microbiome within and between colonies of the threatened Caribbean reef-building coral Acropora palmata in the Florida Keys. Samples were taken from three spatially distinct colony regions--uppermost (high irradiance, underside (low irradiance, and the colony base--representing microhabitats that vary in irradiance and water flow. Phylogenetic diversity (PD values of coral SML bacteria communities were greater than surrounding seawater and lower than adjacent sediment. Bacterial diversity and community composition was consistent among the three microhabitats. Cyanobacteria, Bacteroidetes, Alphaproteobacteria, and Proteobacteria, respectively were the most abundant phyla represented in the samples. This is the first time spatial variability of the surface mucus layer of A. palmata has been studied. Homogeneity in the microbiome of A. palmata contrasts with SML heterogeneity found in other Caribbean corals. These findings suggest that, during non-stressful conditions, host regulation of SML microbiota may override diverse physiochemical influences induced by the topographical complexity of A. palmata. Documenting the spatial distribution of SML microbes is essential to understanding the functional roles these microorganisms play in coral health and adaptability to environmental perturbations.

  12. Spatial Homogeneity of Bacterial Communities Associated with the Surface Mucus Layer of the Reef-Building Coral Acropora palmata.

    Science.gov (United States)

    Kemp, Dustin W; Rivers, Adam R; Kemp, Keri M; Lipp, Erin K; Porter, James W; Wares, John P

    2015-01-01

    Coral surface mucus layer (SML) microbiota are critical components of the coral holobiont and play important roles in nutrient cycling and defense against pathogens. We sequenced 16S rRNA amplicons to examine the structure of the SML microbiome within and between colonies of the threatened Caribbean reef-building coral Acropora palmata in the Florida Keys. Samples were taken from three spatially distinct colony regions--uppermost (high irradiance), underside (low irradiance), and the colony base--representing microhabitats that vary in irradiance and water flow. Phylogenetic diversity (PD) values of coral SML bacteria communities were greater than surrounding seawater and lower than adjacent sediment. Bacterial diversity and community composition was consistent among the three microhabitats. Cyanobacteria, Bacteroidetes, Alphaproteobacteria, and Proteobacteria, respectively were the most abundant phyla represented in the samples. This is the first time spatial variability of the surface mucus layer of A. palmata has been studied. Homogeneity in the microbiome of A. palmata contrasts with SML heterogeneity found in other Caribbean corals. These findings suggest that, during non-stressful conditions, host regulation of SML microbiota may override diverse physiochemical influences induced by the topographical complexity of A. palmata. Documenting the spatial distribution of SML microbes is essential to understanding the functional roles these microorganisms play in coral health and adaptability to environmental perturbations.

  13. Spatial and temporal variation of archaeal, bacterial and fungal communities in agricultural soils

    NARCIS (Netherlands)

    de Cassia Pereira e Silva, Michele; Franco Dias, Armando Cavalcante; van Elsas, Jan Dirk; Salles, Joana Falcao

    2012-01-01

    Background: Soil microbial communities are in constant change at many different temporal and spatial scales. However, the importance of these changes to the turnover of the soil microbial communities has been rarely studied simultaneously in space and time. Methodology/Principal Findings: In this

  14. Biodegradation in a Partially Saturated Sand Matrix: Compounding Effects of Water Content, Bacterial Spatial Distribution, and Motility

    DEFF Research Database (Denmark)

    Dechesne, Arnaud; Owsianiak, Mikolaj; Bazire, Alexis

    2010-01-01

    colonizing these zones or on pollutant mass transfer to neighboring zones containing degraders. In a model system, we quantified the role exerted by water on mineralization rate in the context of a heterogeneously distributed degradation potential. Alginate beads colonized by Pseudomonas putida KT2440 were......Bacterial pesticide degraders are generally heterogeneously distributed in soils, leaving soil volumes devoid of degradation potential. This is expected to have an impact on degradation rates because the degradation of pollutant molecules in such zones will be contingent either on degraders...... inserted at prescribed locations in sand microcosms so that the initial spatial distribution of the mineralization potential was controlled. The mineralization rate was strongly affected by the matric potential (decreasing rate with decreasing matric potential) and by the initial distribution...

  15. Network-scale spatial and temporal variation in Chinook salmon (Oncorhynchus tshawytscha) redd distributions: patterns inferred from spatially continuous replicate surveys

    Science.gov (United States)

    Daniel J. Isaak; Russell F. Thurow

    2006-01-01

    Spatially continuous sampling designs, when temporally replicated, provide analytical flexibility and are unmatched in their ability to provide a dynamic system view. We have compiled such a data set by georeferencing the network-scale distribution of Chinook salmon (Oncorhynchus tshawytscha) redds across a large wilderness basin (7330 km2) in...

  16. "HOOF-Print" Genotyping and Haplotype Inference Discriminates among Brucella spp Isolates From a Small Spatial Scale

    Science.gov (United States)

    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 ...

  17. [Distribution and spatial ordering of biopolymer molecules in resting bacterial spores].

    Science.gov (United States)

    Duda, V I; Korolev, Iu N; El'-Registan, G I; Duzha, M V; Telegin, N L

    1978-01-01

    The presence, distribution and spatial arrangement of biopolymers in situ were studied in both a total intact spore and in a certain cellular layer using a spectroscopic technique of attenuated total refraction (ATR-IR) in the IR region. In contrast to vegetative cells, intact spores were characterized by isotropic distribution of protein components. This feature can be regarded as an index of the cryptobiotic state of spores. However, the distribution of protein components among individual layers of a spore was anisotropic. Bonds characterized by amide I and amide II bands were most often ordered in a layer which comprised cellular structures from the exosporium to the inner spore membrane.

  18. Bacterial community radial-spatial distribution in biofilms along pipe wall in chlorinated drinking water distribution system of East China.

    Science.gov (United States)

    Liu, Jingqing; Ren, Hongxing; Ye, Xianbei; Wang, Wei; Liu, Yan; Lou, Liping; Cheng, Dongqing; He, Xiaofang; Zhou, Xiaoyan; Qiu, Shangde; Fu, Liusong; Hu, Baolan

    2017-01-01

    Biofilms in the pipe wall may lead to water quality deterioration and biological instability in drinking water distribution systems (DWDSs). In this study, bacterial community radial-spatial distribution in biofilms along the pipe wall in a chlorinated DWDS of East China was investigated. Three pipes of large diameter (300, 600, and 600 mm) were sampled in this DWDS, including a ductile cast iron pipe (DCIP) with pipe age of 11 years and two gray cast iron pipes (GCIP) with pipe ages of 17 and 19 years, and biofilms in the upper, middle, and lower parts of each pipe wall were collected. Real-time quantitative polymerase chain reaction (qPCR) and culture-based method were used to quantify bacteria. 454 pyrosequencing was used for bacterial community analysis. The results showed that the biofilm density and total solid (TS) and volatile solid (VS) contents increased gradually from the top to the bottom along the pipe wall. Microorganisms were concentrated in the upper and lower parts of the pipe wall, together accounting for more than 80 % of the total biomass in the biofilms. The bacterial communities in biofilms were significantly different in different areas of the pipe wall and had no strong interaction. Compared with the upper and lower parts of the pipe wall, the bacterial community in the middle of the pipe wall was distributed evenly and had the highest diversity. The 16S rRNA genes of various possible pathogens, including Escherichia coli, Staphylococcus epidermidis, Pseudomonas aeruginosa, and Salmonella enterica, were detected in the biofilms, and the abundances of these possible pathogens were highest in the middle of the pipe wall among three areas. The detachment of the biofilms is the main reason for the deterioration of the water quality in DWDSs. The results of this study suggest that the biofilms in the middle of the pipe wall have highly potential risk for drinking water safety, which provides new ideas for the study of the microbial ecology in

  19. Inferring spatial and temporal behavioral patterns of free-ranging manatees using saltwater sensors of telemetry tags

    Science.gov (United States)

    Castelblanco-Martínez, Delma Nataly; Morales-Vela, Benjamin; Slone, Daniel H.; Padilla-Saldívar, Janneth Adriana; Reid, James P.; Hernández-Arana, Héctor Abuid

    2015-01-01

    Diving or respiratory behavior in aquatic mammals can be used as an indicator of physiological activity and consequently, to infer behavioral patterns. Five Antillean manatees, Trichechus manatus manatus, were captured in Chetumal Bay and tagged with GPS tracking devices. The radios were equipped with a micropower saltwater sensor (SWS), which records the times when the tag assembly was submerged. The information was analyzed to establish individual fine-scale behaviors. For each fix, we established the following variables: distance (D), sampling interval (T), movement rate (D/T), number of dives (N), and total diving duration (TDD). We used logic criteria and simple scatterplots to distinguish between behavioral categories: ‘Travelling’ (D/T ≥ 3 km/h), ‘Surface’ (↓TDD, ↓N), ‘Bottom feeding’ (↑TDD, ↑N) and ‘Bottom resting’ (↑TDD, ↓N). Habitat categories were qualitatively assigned: Lagoon, Channels, Caye shore, City shore, Channel edge, and Open areas. The instrumented individuals displayed a daily rhythm of bottom activities, with surfacing activities more frequent during the night and early in the morning. More investigation into those cycles and other individual fine-scale behaviors related to their proximity to concentrations of human activity would be informative

  20. Ecosystem-level water-use efficiency inferred from eddy covariance data: definitions, patterns and spatial up-scaling

    Science.gov (United States)

    Reichstein, M.; Beer, C.; Kuglitsch, F.; Papale, D.; Soussana, J. A.; Janssens, I.; Ciais, P.; Baldocchi, D.; Buchmann, N.; Verbeeck, H.; Ceulemans, R.; Moors, E.; Köstner, B.; Schulze, D.; Knohl, A.; Law, B. E.

    2007-12-01

    In this presentation we discuss ways to infer and to interpret water-use efficiency at ecosystem level (WUEe) from eddy covariance flux data and possibilities for scaling these patterns to regional and continental scale. In particular we convey the following: WUEe may be computed as a ratio of integrated fluxes or as the slope of carbon versus water fluxes offering different chances for interpretation. If computed from net ecosystem exchange and evapotranspiration on has to take of counfounding effects of respiration and soil evaporation. WUEe time-series at diurnal and seasonal scale is a valuable ecosystem physiological diagnostic for example about ecosystem-level responses to drought. Most often WUEe decreases during dry periods. The mean growing season ecosystem water-use efficiency of gross carbon uptake (WUEGPP) is highest in temperate broad-leaved deciduous forests, followed by temperate mixed forests, temperate evergreen conifers, Mediterranean broad-leaved deciduous forests, Mediterranean broad-leaved evergreen forests and Mediterranean evergreen conifers and boreal, grassland and tundra ecosystems. Water-use efficiency exhibits a temporally quite conservative relation with atmospheric water vapor pressure deficit (VPD) that is modified between sites by leaf area index (LAI) and soil quality, such that WUEe increases with LAI and soil water holding capacity which is related to texture. This property and tight coupling between carbon and water cycles is used to estimate catchment-scale water-use efficiency and primary productivity by integration of space-borne earth observation and river discharge data.

  1. Evaluating Spatial Variability in Sediment and Phosphorus Concentration-Discharge Relationships Using Bayesian Inference and Self-Organizing Maps

    Science.gov (United States)

    Underwood, Kristen L.; Rizzo, Donna M.; Schroth, Andrew W.; Dewoolkar, Mandar M.

    2017-12-01

    Given the variable biogeochemical, physical, and hydrological processes driving fluvial sediment and nutrient export, the water science and management communities need data-driven methods to identify regions prone to production and transport under variable hydrometeorological conditions. We use Bayesian analysis to segment concentration-discharge linear regression models for total suspended solids (TSS) and particulate and dissolved phosphorus (PP, DP) using 22 years of monitoring data from 18 Lake Champlain watersheds. Bayesian inference was leveraged to estimate segmented regression model parameters and identify threshold position. The identified threshold positions demonstrated a considerable range below and above the median discharge—which has been used previously as the default breakpoint in segmented regression models to discern differences between pre and post-threshold export regimes. We then applied a Self-Organizing Map (SOM), which partitioned the watersheds into clusters of TSS, PP, and DP export regimes using watershed characteristics, as well as Bayesian regression intercepts and slopes. A SOM defined two clusters of high-flux basins, one where PP flux was predominantly episodic and hydrologically driven; and another in which the sediment and nutrient sourcing and mobilization were more bimodal, resulting from both hydrologic processes at post-threshold discharges and reactive processes (e.g., nutrient cycling or lateral/vertical exchanges of fine sediment) at prethreshold discharges. A separate DP SOM defined two high-flux clusters exhibiting a bimodal concentration-discharge response, but driven by differing land use. Our novel framework shows promise as a tool with broad management application that provides insights into landscape drivers of riverine solute and sediment export.

  2. Food-web inferences of stable isotope spatial patterns in copepods and yellowfin tuna in the pelagic eastern Pacific Ocean

    Science.gov (United States)

    Olson, Robert J.; Popp, Brian N.; Graham, Brittany S.; López-Ibarra, Gladis A.; Galván-Magaña, Felipe; Lennert-Cody, Cleridy E.; Bocanegra-Castillo, Noemi; Wallsgrove, Natalie J.; Gier, Elizabeth; Alatorre-Ramírez, Vanessa; Ballance, Lisa T.; Fry, Brian

    2010-07-01

    Evaluating the impacts of climate and fishing on oceanic ecosystems requires an improved understanding of the trophodynamics of pelagic food webs. Our approach was to examine broad-scale spatial relationships among the stable N isotope values of copepods and yellowfin tuna ( Thunnus albacares), and to quantify yellowfin tuna trophic status in the food web based on stable-isotope and stomach-contents analyses. Using a generalized additive model fitted to abundance-weighted-average δ 15N values of several omnivorous copepod species, we examined isotopic spatial relationships among yellowfin tuna and copepods. We found a broad-scale, uniform gradient in δ 15N values of copepods increasing from south to north in a region encompassing the eastern Pacific warm pool and parts of several current systems. Over the same region, a similar trend was observed for the δ 15N values in the white muscle of yellowfin tuna caught by the purse-seine fishery, implying limited movement behavior. Assuming the omnivorous copepods represent a proxy for the δ 15N values at the base of the food web, the isotopic difference between these two taxa, “ ΔYFT-COP,” was interpreted as a trophic-position offset. Yellowfin tuna trophic-position estimates based on their bulk δ 15N values were not significantly different than independent estimates based on stomach contents, but are sensitive to errors in the trophic enrichment factor and the trophic position of copepods. An apparent inshore-offshore, east to west gradient in yellowfin tuna trophic position was corroborated using compound-specific isotope analysis of amino acids conducted on a subset of samples. The gradient was not explained by the distribution of yellowfin tuna of different sizes, by seasonal variability at the base of the food web, or by known ambit distances (i.e. movements). Yellowfin tuna stomach contents did not show a regular inshore-offshore gradient in trophic position during 2003-2005, but the trophic

  3. Spatial variations of bacterial community and its relationship with water chemistry in Sanya Bay, South China Sea as determined by DGGE fingerprinting and multivariate analysis.

    Science.gov (United States)

    Ling, Juan; Zhang, Yan-Ying; Dong, Jun-De; Wang, You-Shao; Feng, Jing-Bing; Zhou, Wei-Hua

    2015-10-01

    Bacteria play important roles in the structure and function of marine food webs by utilizing nutrients and degrading the pollutants, and their distribution are determined by surrounding water chemistry to a certain extent. It is vital to investigate the bacterial community's structure and identifying the significant factors by controlling the bacterial distribution in the paper. Flow cytometry showed that the total bacterial abundance ranged from 5.27 × 10(5) to 3.77 × 10(6) cells/mL. Molecular fingerprinting technique, denaturing gradient gel electrophoresis (DGGE) followed by DNA sequencing has been employed to investigate the bacterial community composition. The results were then interpreted through multivariate statistical analysis and tended to explain its relationship to the environmental factors. A total of 270 bands at 83 different positions were detected in DGGE profiles and 29 distinct DGGE bands were sequenced. The predominant bacteria were related to Phyla Protebacteria species (31 %, nine sequences), Cyanobacteria (37.9 %, eleven sequences) and Actinobacteria (17.2 %, five sequences). Other phylogenetic groups identified including Firmicutes (6.9 %, two sequences), Bacteroidetes (3.5 %, one sequences) and Verrucomicrobia (3.5 %, one sequences). Conical correspondence analysis was used to elucidate the relationships between the bacterial community compositions and environmental factors. The results showed that the spatial variations in the bacterial community composition was significantly related to phosphate (P = 0.002, P < 0.01), dissolved organic carbon (P = 0.004, P < 0.01), chemical oxygen demand (P = 0.010, P < 0.05) and nitrite (P = 0.016, P < 0.05). This study revealed the spatial variations of bacterial community and significant environmental factors driving the bacterial composition shift. These results may be valuable for further investigation on the functional microbial structure and expression quantitatively under the polluted

  4. Subsurface mapping of Rustenburg Layered Suite (RLS), Bushveld Complex, South Africa: Inferred structural features using borehole data and spatial analysis

    Science.gov (United States)

    Bamisaiye, O. A.; Eriksson, P. G.; Van Rooy, J. L.; Brynard, H. M.; Foya, S.; Billay, A. Y.; Nxumalo, V.

    2017-08-01

    Faults and other structural features within the mafic-ultramafic layers of the Bushveld Complex have been a major issue mainly for exploration and mine planning. This study employed a new approach in detecting faults with both regional and meter scale offsets, which was not possible with the usually applied structure contour mapping. Interpretations of faults from structural and isopach maps were previously based on geological experience, while meter-scale faults were virtually impossible to detect from such maps. Spatial analysis was performed using borehole data primarily. This resulted in the identification of previously known structures and other hitherto unsuspected structural features. Consequently, the location, trends, and geometry of faults and some regional features within the Rustenburg Layered Suite (RLS) that might not be easy to detect through field mapping are adequately described in this study.

  5. Spatial Distribution of Reef Fish Species along the Southeast US Atlantic Coast Inferred from Underwater Video Survey Data.

    Directory of Open Access Journals (Sweden)

    Nathan M Bacheler

    Full Text Available Marine fish abundance and distribution often varies across spatial scales for a variety of reasons, and this variability has significant ecological and management consequences. We quantified the distribution of reef-associated fish species along the southeast United States Atlantic coast using underwater video survey samples (N = 4,855 in 2011-2014 to elucidate variability within species across space, depths, and habitats, as well as describe broad-scale patterns in species richness. Thirty-two species were seen at least 10 times on video, and the most commonly observed species were red porgy (Pagrus pagrus; 41.4% of videos, gray triggerfish (Balistes capriscus; 31.0%, black sea bass (Centropristis striata; 29.1%, vermilion snapper (Rhomboplites aurorubens; 27.7%, and red snapper (Lutjanus campechanus; 22.6%. Using generalized additive models, we found that most species were non-randomly distributed across space, depths, and habitats. Most rare species were observed along the continental shelf break, except for goliath grouper (Epinephelus itajara, which was found on the continental shelf in Florida and Georgia. We also observed higher numbers of species in shelf-break habitats from southern North Carolina to Georgia, and fewer in shallower water and at the northern and southern ends of the southeast United States Atlantic coast. Our study provides the first broad-scale description of the spatial distribution of reef fish in the region to be based on fishery-independent data, reinforces the utility of underwater video to survey reef fish, and can help improve the management of reef fish in the SEUS, for example, by improving indices of abundance.

  6. Spatial and Species Variations in Bacterial Communities Associated with Corals from the Red Sea as Revealed by Pyrosequencing

    KAUST Repository

    Lee, O. O.; Yang, J.; Bougouffa, S.; Wang, Y.; Batang, Zenon B.; Tian, R.; Al-Suwailem, A.; Qian, P.-Y.

    2012-01-01

    -pyrosequencing technique to investigate the bacterial communities associated with three stony Scleractinea and two soft Octocorallia corals from three locations in the Red Sea. Our results revealed highly diverse bacterial communities in the Red Sea corals, with more than

  7. Spatial and trophic preferences of jumbo squid Dosidicus gigas (D´Orbigny, 1835) in the central Gulf of California: ecological inferences using stable isotopes.

    Science.gov (United States)

    Trasviña-Carrillo, L D; Hernández-Herrera, A; Torres-Rojas, Y E; Galván-Magaña, F; Sánchez-González, A; Aguíñiga-García, S

    2018-04-26

    The jumbo squid Dosidicus gigas is a fishery resource of considerable economic and ecological importance in the Mexican Pacific. Studies on its habitat preferences are needed to understand recent fluctuations in the species' abundance and availability. Stable isotope analysis allows us to infer ecological aspects such as spatial distribution and trophic preferences. We used an isotope ratio mass spectrometer, automated for carbonate analysis, and coupled to an elemental analyzer, to determine the isotopic composition of statoliths (δ 18 O and δ 13 C values) and beaks (δ 13 C and δ 15 N values) from 219 individuals caught over two fishing seasons (2007 and 2009) off the coast of Santa Rosalía, in the central Gulf of California. We used these isotopic ratios to assess variation in spatial and trophic preferences by sex, size, and fishing season. In the 2009 group, we observed significant differences in statolith δ 13 C values and beak δ 13 C and δ 15 N values between males and females. Between size groups, we observed significant differences in statolith δ 18 O and δ 13 C values in 2007 and in beak δ 13 C and δ 15 N values during both seasons. Both seasons were characterized by high overlap in δ 18 O and δ 13 C values between sexes and in 2009 between size groups. We observed low trophic overlap between sexes in 2009 and between size groups during both seasons. The isotopic ratios from statoliths and beaks indicate that D. gigas has changed its spatial and trophic preferences, a shift that is probably related to changes in the species diet. This intraspecific variation in preferences could be related to characteristics such as size, which may influence squid distribution preferences. This article is protected by copyright. All rights reserved.

  8. Mass distribution and spatial organization of the linear bacterial motor of Spiroplasma citri R8A2.

    Science.gov (United States)

    Trachtenberg, Shlomo; Andrews, S Brian; Leapman, Richard D

    2003-03-01

    In the simple, helical, wall-less bacterial genus Spiroplasma, chemotaxis and motility are effected by a linear, contractile motor arranged as a flat cytoskeletal ribbon attached to the inner side of the membrane along the shortest helical line. With scanning transmission electron microscopy and diffraction analysis, we determined the hierarchical and spatial organization of the cytoskeleton of Spiroplasma citri R8A2. The structural unit appears to be a fibril, approximately 5 nm wide, composed of dimers of a 59-kDa protein; each ribbon is assembled from seven fibril pairs. The functional unit of the intact ribbon is a pair of aligned fibrils, along which pairs of dimers form tetrameric ring-like repeats. On average, isolated and purified ribbons contain 14 fibrils or seven well-aligned fibril pairs, which are the same structures observed in the intact cell. Scanning transmission electron microscopy mass analysis and sodium dodecyl sulfate-polyacrylamide gel electrophoresis of purified cytoskeletons indicate that the 59-kDa protein is the only constituent of the ribbons.

  9. Differential sharing and distinct co-occurrence networks among spatially close bacterial microbiota of bark, mosses and lichens‬‬.

    Science.gov (United States)

    Aschenbrenner, Ines Aline; Cernava, Tomislav; Erlacher, Armin; Berg, Gabriele; Grube, Martin

    2017-05-01

    Knowledge of bacterial community host-specificity has increased greatly in recent years. However, the intermicrobiome relationships of unrelated but spatially close organisms remain little understood. Trunks of trees covered by epiphytes represent complex habitats with a mosaic of ecological niches. In this context, we investigated the structure, diversity and interactions of microbiota associated with lichens, mosses and the bare tree bark. Comparative analysis revealed significant differences in the habitat-associated community structures. Corresponding co-occurrence analysis indicated that the lichen microbial network is less complex and less densely interconnected than the moss- and bark-associated networks. Several potential generalists and specialists were identified for the selected habitats. Generalists belonged mainly to Proteobacteria, with Sphingomonas as the most abundant genus. The generalists comprise microorganisms with generally beneficial features, such as nitrogen fixation or other supporting functions, according to a metagenomic analysis. We argue that beneficial strains shared among hosts contribute to ecological stability of the host biocoenoses. © 2017 John Wiley & Sons Ltd.

  10. Estimation of spatially varying heat transfer coefficient from a flat plate with flush mounted heat sources using Bayesian inference

    Science.gov (United States)

    Jakkareddy, Pradeep S.; Balaji, C.

    2016-09-01

    This paper employs the Bayesian based Metropolis Hasting - Markov Chain Monte Carlo algorithm to solve inverse heat transfer problem of determining the spatially varying heat transfer coefficient from a flat plate with flush mounted discrete heat sources with measured temperatures at the bottom of the plate. The Nusselt number is assumed to be of the form Nu = aReb(x/l)c . To input reasonable values of ’a’ and ‘b’ into the inverse problem, first limited two dimensional conjugate convection simulations were done with Comsol. Based on the guidance from this different values of ‘a’ and ‘b’ are input to a computationally less complex problem of conjugate conduction in the flat plate (15mm thickness) and temperature distributions at the bottom of the plate which is a more convenient location for measuring the temperatures without disturbing the flow were obtained. Since the goal of this work is to demonstrate the eficiacy of the Bayesian approach to accurately retrieve ‘a’ and ‘b’, numerically generated temperatures with known values of ‘a’ and ‘b’ are treated as ‘surrogate’ experimental data. The inverse problem is then solved by repeatedly using the forward solutions together with the MH-MCMC aprroach. To speed up the estimation, the forward model is replaced by an artificial neural network. The mean, maximum-a-posteriori and standard deviation of the estimated parameters ‘a’ and ‘b’ are reported. The robustness of the proposed method is examined, by synthetically adding noise to the temperatures.

  11. Temporal-spatial structure of magnetic merging at the magnetopause inferred from 557.7-nm all-sky images

    Directory of Open Access Journals (Sweden)

    N. C. Maynard

    2004-09-01

    Full Text Available We demonstrate that high-resolution 557.7-nm all-sky images are useful tools for investigating the spatial and temporal evolution of merging on the dayside magnetopause. Analysis of ground and satellite measurements leads us to conclude that high-latitude merging events can occur at multiple sites simultaneously and vary asynchronously on time scales of 30s to 3min. Variations of 557.7nm emissions were observed at a 10s cadence at Ny-Ålesund on 19 December 2001, while significant changes in the IMF clock angle were reaching the magnetopause. The optical patterns are consistent with a scenario in which merging occurs around the rim of the high-latitude cusp at positions dictated by the IMF clock angle. Electrons energized at merging sites represent plausible sources for 557.7nm emissions in the cusp. Polar observations at the magnetopause have directly linked enhanced fluxes of ≥0.5keV electrons with merging. Spectra of electrons responsible for some of the emissions, measured during a DMSP F15 overflight, exhibit "inverted-V" features, indicating further acceleration above the ionosphere. SuperDARN spectral width boundaries, characteristic of open-closed field line transitions, are located at the equatorward edge of the 557.7nm emissions. Optical data suggest that with IMF BY>0, the Northern Hemisphere cusp divides into three source regions. When the IMF clock angle was ~150° structured 557.7-nm emissions came from east of the 13:00 MLT meridian. At larger clock angles the emissions appeared between 12:00 and 13:00 MLT. No significant 557.7-nm emissions were detected in the prenoon MLT sector. MHD simulations corroborate our scenario, showing that with the observed large dipole-tilt and IMF clock angles, merging sites develop near the front and eastern portions of the high-altitude cusp rim in the Northern Hemisphere and near the western part of the cusp rim in the Southern Hemisphere.

  12. Spatial prediction of landslide susceptibility using an adaptive neuro-fuzzy inference system combined with frequency ratio, generalized additive model, and support vector machine techniques

    Science.gov (United States)

    Chen, Wei; Pourghasemi, Hamid Reza; Panahi, Mahdi; Kornejady, Aiding; Wang, Jiale; Xie, Xiaoshen; Cao, Shubo

    2017-11-01

    The spatial prediction of landslide susceptibility is an important prerequisite for the analysis of landslide hazards and risks in any area. This research uses three data mining techniques, such as an adaptive neuro-fuzzy inference system combined with frequency ratio (ANFIS-FR), a generalized additive model (GAM), and a support vector machine (SVM), for landslide susceptibility mapping in Hanyuan County, China. In the first step, in accordance with a review of the previous literature, twelve conditioning factors, including slope aspect, altitude, slope angle, topographic wetness index (TWI), plan curvature, profile curvature, distance to rivers, distance to faults, distance to roads, land use, normalized difference vegetation index (NDVI), and lithology, were selected. In the second step, a collinearity test and correlation analysis between the conditioning factors and landslides were applied. In the third step, we used three advanced methods, namely, ANFIS-FR, GAM, and SVM, for landslide susceptibility modeling. Subsequently, the results of their accuracy were validated using a receiver operating characteristic curve. The results showed that all three models have good prediction capabilities, while the SVM model has the highest prediction rate of 0.875, followed by the ANFIS-FR and GAM models with prediction rates of 0.851 and 0.846, respectively. Thus, the landslide susceptibility maps produced in the study area can be applied for management of hazards and risks in landslide-prone Hanyuan County.

  13. Spatial distribution of bacterial communities on volumetric and planar anodes in single-chamber air-cathode microbial fuel cells

    KAUST Repository

    Vargas, Ignacio T.; Albert, Istvan U.; Regan, John M.

    2013-01-01

    Pyrosequencing was used to characterize bacterial communities in air-cathode microbial fuel cells across a volumetric (graphite fiber brush) and a planar (carbon cloth) anode, where different physical and chemical gradients would be expected

  14. Spatial and Species Variations in Bacterial Communities Associated with Corals from the Red Sea as Revealed by Pyrosequencing

    KAUST Repository

    Lee, O. O.

    2012-08-03

    Microbial associations with corals are common and are most likely symbiotic, although their diversity and relationships with environmental factors and host species remain unclear. In this study, we adopted a 16S rRNA gene tag-pyrosequencing technique to investigate the bacterial communities associated with three stony Scleractinea and two soft Octocorallia corals from three locations in the Red Sea. Our results revealed highly diverse bacterial communities in the Red Sea corals, with more than 600 ribotypes detected and up to 1,000 species estimated from a single coral species. Altogether, 21 bacterial phyla were recovered from the corals, of which Gammaproteobacteria was the most dominant group, and Chloroflexi, Chlamydiae, and the candidate phylum WS3 were reported in corals for the first time. The associated bacterial communities varied greatly with location, where environmental conditions differed significantly. Corals from disturbed areas appeared to share more similar bacterial communities, but larger variations in community structures were observed between different coral species from pristine waters. Ordination methods identified salinity and depth as the most influential parameters affecting the abundance of Vibrio, Pseudoalteromonas, Serratia, Stenotrophomonas, Pseudomonas, and Achromobacter in the corals. On the other hand, bacteria such as Chloracidobacterium and Endozoicomonas were more sensitive to the coral species, suggesting that the host species type may be influential in the associated bacterial community, as well. The combined influences of the coral host and environmental factors on the associated microbial communities are discussed. This study represents the first comparative study using tag-pyrosequencing technology to investigate the bacterial communities in Red Sea corals.

  15. Spatial and temporal variations in bacterial macromolecule labeling with [methyl-3H]thymidine in a hypertrophic lake

    International Nuclear Information System (INIS)

    Robarts, R.D.; Wicks, R.J.; Sephton, L.M.

    1986-01-01

    The incorporation of [methyl- 3 H]thymidine into three macromolecular fractions, designated as DNA, RNA, and protein, by bacteria from Hartbeespoort Dam, South Africa, was measured over 1 year by acid-base hydrolysis procedures. Samples were collected at 10 m, which was at least 5 m beneath the euphotic zone. On four occasions, samples were concurrently collected at the surface. Approximately 80% of the label was incorporated into bacterial DNA in surface samples. At 10 m, total incorporation of label into bacterial macromolecules was correlated to bacterial utilization of glucose. The labeling of DNA, which ranged between 0 and 78% of total macromolecule incorporation, was inversely related to glucose uptake, total thymidine incorporation, and euphotic zone algal production. With decreased DNA labeling, increasing proportions of label were found in the RNA fraction and proteins. Enzymatic digestion followed by chromatographic separation of macromolecule fragments indicated that DNA and proteins were labeled while RNA was not. The RNA fraction may represent labeled lipids or other macromolecules or both. The data demonstrated a close coupling between phytoplankton production and heterotrophic bacterial activity in this hypertrophic lake but also confirmed the need for the routine extraction and purification of DNA during [methyl- 3 H]thymidine studies of aquatic bacterial production

  16. Assessment of temporal and spatial evolution of bacterial communities in a biological sand filter mesocosm treating winery wastewater.

    Science.gov (United States)

    Ramond, J-B; Welz, P J; Tuffin, M I; Burton, S G; Cowan, D A

    2013-07-01

    To assess the impact of winery wastewater (WW) on biological sand filter (BSF) bacterial community structures, and to evaluate whether BSFs can constitute alternative and valuable treatment- processes to remediate WW. During 112 days, WW was used to contaminate a BSF mesocosm (length 173 cm/width 106 cm/depth 30 cm). The effect of WW on bacterial communities of four BSF microenvironments (surface/deep, inlet/outlet) was investigated using terminal-restriction fragment length polymorphism (T-RFLP). BSF achieved high Na (95·1%), complete Cl and almost complete chemical oxygen demand (COD) (98·0%) and phenolic (99·2%) removals. T-RFLP analysis combined with anosim revealed that WW significantly modified the surface and deep BSF bacterial communities. BSF provided high COD, phenolic and salt removals throughout the experiment. WW-selected bacterial communities were thus able to tolerate and/or degrade WW, suggesting that community composition does not alter BSF performances. However, biomass increased significantly in the WW-impacted surface sediments, which could later lead to system clogging and should thus be monitored. BSFs constitute alternatives to constructed wetlands to treat agri effluents such as WW. To our knowledge, this study is the first unravelling the responses of BSF bacterial communities to contamination and suggests that WW-selected BSF communities maintained high removal performances. Journal of Applied Microbiology © 2013 The Society for Applied Microbiology.

  17. Entropic Inference

    Science.gov (United States)

    Caticha, Ariel

    2011-03-01

    In this tutorial we review the essential arguments behing entropic inference. We focus on the epistemological notion of information and its relation to the Bayesian beliefs of rational agents. The problem of updating from a prior to a posterior probability distribution is tackled through an eliminative induction process that singles out the logarithmic relative entropy as the unique tool for inference. The resulting method of Maximum relative Entropy (ME), includes as special cases both MaxEnt and Bayes' rule, and therefore unifies the two themes of these workshops—the Maximum Entropy and the Bayesian methods—into a single general inference scheme.

  18. Spatial scale affects the relative role of stochasticity versus determinism in soil bacterial communities in wheat fields across the North China Plain.

    Science.gov (United States)

    Shi, Yu; Li, Yuntao; Xiang, Xingjia; Sun, Ruibo; Yang, Teng; He, Dan; Zhang, Kaoping; Ni, Yingying; Zhu, Yong-Guan; Adams, Jonathan M; Chu, Haiyan

    2018-02-05

    The relative importance of stochasticity versus determinism in soil bacterial communities is unclear, as are the possible influences that alter the balance between these. Here, we investigated the influence of spatial scale on the relative role of stochasticity and determinism in agricultural monocultures consisting only of wheat, thereby minimizing the influence of differences in plant species cover and in cultivation/disturbance regime, extending across a wide range of soils and climates of the North China Plain (NCP). We sampled 243 sites across 1092 km and sequenced the 16S rRNA bacterial gene using MiSeq. We hypothesized that determinism would play a relatively stronger role at the broadest scales, due to the strong influence of climate and soil differences in selecting many distinct OTUs of bacteria adapted to the different environments. In order to test the more general applicability of the hypothesis, we also compared with a natural ecosystem on the Tibetan Plateau. Our results revealed that the relative importance of stochasticity vs. determinism did vary with spatial scale, in the direction predicted. On the North China Plain, stochasticity played a dominant role from 150 to 900 km (separation between pairs of sites) and determinism dominated at more than 900 km (broad scale). On the Tibetan Plateau, determinism played a dominant role from 130 to 1200 km and stochasticity dominated at less than 130 km. Among the identifiable deterministic factors, soil pH showed the strongest influence on soil bacterial community structure and diversity across the North China Plain. Together, 23.9% of variation in soil microbial community composition could be explained, with environmental factors accounting for 19.7% and spatial parameters 4.1%. Our findings revealed that (1) stochastic processes are relatively more important on the North China Plain, while deterministic processes are more important on the Tibetan Plateau; (2) soil pH was the major factor in shaping

  19. Distributional Inference

    NARCIS (Netherlands)

    Kroese, A.H.; van der Meulen, E.A.; Poortema, Klaas; Schaafsma, W.

    1995-01-01

    The making of statistical inferences in distributional form is conceptionally complicated because the epistemic 'probabilities' assigned are mixtures of fact and fiction. In this respect they are essentially different from 'physical' or 'frequency-theoretic' probabilities. The distributional form is

  20. Entropic Inference

    OpenAIRE

    Caticha, Ariel

    2010-01-01

    In this tutorial we review the essential arguments behing entropic inference. We focus on the epistemological notion of information and its relation to the Bayesian beliefs of rational agents. The problem of updating from a prior to a posterior probability distribution is tackled through an eliminative induction process that singles out the logarithmic relative entropy as the unique tool for inference. The resulting method of Maximum relative Entropy (ME), includes as special cases both MaxEn...

  1. Spatial patterns of bacterial abundance, activity and community composition in relation to water masses in the eastern Mediterranean Sea

    NARCIS (Netherlands)

    Yokokawa, Taichi; De Corte, Daniele; Sintes, Eva; Herndl, Gerhard J.

    2010-01-01

    To determine the variation of bacterial activity and community composition between and within specific water masses, samples were collected throughout the water column at 5 stations in the eastern Mediterranean Sea corresponding to the regions of the northern Aegean, mid-Aegean, western Cretan,

  2. Clonal structure in Ichthyobacterium seriolicida, the causative agent of bacterial haemolytic jaundice in yellowtail, Seriola quinqueradiata, inferred from molecular epidemiological analysis.

    Science.gov (United States)

    Matsuyama, T; Fukuda, Y; Sakai, T; Tanimoto, N; Nakanishi, M; Nakamura, Y; Takano, T; Nakayasu, C

    2017-08-01

    Bacterial haemolytic jaundice caused by Ichthyobacterium seriolicida has been responsible for mortality in farmed yellowtail, Seriola quinqueradiata, in western Japan since the 1980s. In this study, polymorphic analysis of I. seriolicida was performed using three molecular methods: amplified fragment length polymorphism (AFLP) analysis, multilocus sequence typing (MLST) and multiple-locus variable-number tandem repeat analysis (MLVA). Twenty-eight isolates were analysed using AFLP, while 31 isolates were examined by MLST and MLVA. No polymorphisms were identified by AFLP analysis using EcoRI and MseI, or by MLST of internal fragments of eight housekeeping genes. However, MLVA revealed variation in repeat numbers of three elements, allowing separation of the isolates into 16 sequence types. The unweighted pair group method using arithmetic averages cluster analysis of the MLVA data identified four major clusters, and all isolates belonged to clonal complexes. It is likely that I. seriolicida populations share a common ancestor, which may be a recently introduced strain. © 2016 John Wiley & Sons Ltd.

  3. Perceptual inference.

    Science.gov (United States)

    Aggelopoulos, Nikolaos C

    2015-08-01

    Perceptual inference refers to the ability to infer sensory stimuli from predictions that result from internal neural representations built through prior experience. Methods of Bayesian statistical inference and decision theory model cognition adequately by using error sensing either in guiding action or in "generative" models that predict the sensory information. In this framework, perception can be seen as a process qualitatively distinct from sensation, a process of information evaluation using previously acquired and stored representations (memories) that is guided by sensory feedback. The stored representations can be utilised as internal models of sensory stimuli enabling long term associations, for example in operant conditioning. Evidence for perceptual inference is contributed by such phenomena as the cortical co-localisation of object perception with object memory, the response invariance in the responses of some neurons to variations in the stimulus, as well as from situations in which perception can be dissociated from sensation. In the context of perceptual inference, sensory areas of the cerebral cortex that have been facilitated by a priming signal may be regarded as comparators in a closed feedback loop, similar to the better known motor reflexes in the sensorimotor system. The adult cerebral cortex can be regarded as similar to a servomechanism, in using sensory feedback to correct internal models, producing predictions of the outside world on the basis of past experience. Copyright © 2015 Elsevier Ltd. All rights reserved.

  4. Spatial distribution of bacterial communities on volumetric and planar anodes in single-chamber air-cathode microbial fuel cells

    KAUST Repository

    Vargas, Ignacio T.

    2013-05-29

    Pyrosequencing was used to characterize bacterial communities in air-cathode microbial fuel cells across a volumetric (graphite fiber brush) and a planar (carbon cloth) anode, where different physical and chemical gradients would be expected associated with the distance between anode location and the air cathode. As expected, the stable operational voltage and the coulombic efficiency (CE) were higher for the volumetric anode than the planar anode (0.57V and CE=22% vs. 0.51V and CE=12%). The genus Geobacter was the only known exoelectrogen among the observed dominant groups, comprising 57±4% of recovered sequences for the brush and 27±5% for the carbon-cloth anode. While the bacterial communities differed between the two anode materials, results showed that Geobacter spp. and other dominant bacterial groups were homogenously distributed across both planar and volumetric anodes. This lends support to previous community analysis interpretations based on a single biofilm sampling location in these systems. © 2013 Wiley Periodicals, Inc.

  5. New insights into the spatial variability of biofilm communities and potentially negative bacterial groups in hydraulic concrete structures.

    Science.gov (United States)

    Cai, Wei; Li, Yi; Niu, Lihua; Zhang, Wenlong; Wang, Chao; Wang, Peifang; Meng, Fangang

    2017-10-15

    The composition and distribution characteristics of bacterial communities in biofilms attached to hydraulic concrete structure (HCS) surfaces were investigated for the first time in four reservoirs in the middle and lower reaches of the Yangtze River Basin using 16S rRNA Miseq sequencing. High microbial diversity was found in HCS biofilms, and notable differences were observed in different types of HCS. Proteobacteria, Cyanobacteria and Chloroflexi were the predominant phyla, with respective relative abundances of 35.3%, 25.4% and 13.0%. The three most abundant genera were Leptolyngbya, Anaerolineaceae and Polynucleobacter. The phyla Beta-proteobacteria and Firmicutes and genus Lyngbya were predominant in CGP, whereas the phyla Cyanobacteria and Chloroflexi and genera Leptolyngbya, Anaerolinea and Polynucleobacter survived better in land walls and bank slopes. Dissolved oxygen, ammonia nitrogen and temperature were characterized as the main factors driving the bacterial community composition. The most abundant groups of metabolic functions were also identified as ammonia oxidizers, sulphate reducers, and dehalogenators. Additionally, functional groups related to biocorrosion were found to account for the largest proportion (14.0% of total sequences) in gate piers, followed by those in land walls (11.5%) and bank slopes (10.2%). Concrete gate piers were at the greatest risk of biocorrosion with the most abundant negative bacterial groups, especially for sulphate reducers. Thus, it should be paid high attention to the biocorrosion prevention of concrete gate piers. Overall, this study contributed to the optimization of microbial control and the improvement of the safety management for water conservation structures. Copyright © 2017 Elsevier Ltd. All rights reserved.

  6. Bacterial communities of two ubiquitous Great Barrier Reef corals reveals both site- and species-specificity of common bacterial associates.

    Directory of Open Access Journals (Sweden)

    E Charlotte E Kvennefors

    Full Text Available BACKGROUND: Coral-associated bacteria are increasingly considered to be important in coral health, and altered bacterial community structures have been linked to both coral disease and bleaching. Despite this, assessments of bacterial communities on corals rarely apply sufficient replication to adequately describe the natural variability. Replicated data such as these are crucial in determining potential roles of bacteria on coral. METHODOLOGY/PRINCIPAL FINDINGS: Denaturing Gradient Gel Electrophoresis (DGGE of the V3 region of the 16S ribosomal DNA was used in a highly replicated approach to analyse bacterial communities on both healthy and diseased corals. Although site-specific variations in the bacterial communities of healthy corals were present, host species-specific bacterial associates within a distinct cluster of gamma-proteobacteria could be identified, which are potentially linked to coral health. Corals affected by "White Syndrome" (WS underwent pronounced changes in their bacterial communities in comparison to healthy colonies. However, the community structure and bacterial ribotypes identified in diseased corals did not support the previously suggested theory of a bacterial pathogen as the causative agent of the syndrome. CONCLUSIONS/SIGNIFICANCE: This is the first study to employ large numbers of replicated samples to assess the bacterial communities of healthy and diseased corals, and the first culture-independent assessment of bacterial communities on WS affected Acroporid corals on the GBR. Results indicate that a minimum of 6 replicate samples are required in order to draw inferences on species, spatial or health-related changes in community composition, as a set of clearly distinct bacterial community profiles exist in healthy corals. Coral bacterial communities may be both site and species specific. Furthermore, a cluster of gamma-proteobacterial ribotypes may represent a group of specific common coral and marine

  7. Entrainment and Control of Bacterial Populations: An in Silico Study over a Spatially Extended Agent Based Model.

    Science.gov (United States)

    Mina, Petros; Tsaneva-Atanasova, Krasimira; Bernardo, Mario di

    2016-07-15

    We extend a spatially explicit agent based model (ABM) developed previously to investigate entrainment and control of the emergent behavior of a population of synchronized oscillating cells in a microfluidic chamber. Unlike most of the work in models of control of cellular systems which focus on temporal changes, we model individual cells with spatial dependencies which may contribute to certain behavioral responses. We use the model to investigate the response of both open loop and closed loop strategies, such as proportional control (P-control), proportional-integral control (PI-control) and proportional-integral-derivative control (PID-control), to heterogeinities and growth in the cell population, variations of the control parameters and spatial effects such as diffusion in the spatially explicit setting of a microfluidic chamber setup. We show that, as expected from the theory of phase locking in dynamical systems, open loop control can only entrain the cell population in a subset of forcing periods, with a wide variety of dynamical behaviors obtained outside these regions of entrainment. Closed-loop control is shown instead to guarantee entrainment in a much wider region of control parameter space although presenting limitations when the population size increases over a certain threshold. In silico tracking experiments are also performed to validate the ability of classical control approaches to achieve other reference behaviors such as a desired constant output or a linearly varying one. All simulations are carried out in BSim, an advanced agent-based simulator of microbial population which is here extended ad hoc to include the effects of control strategies acting onto the population.

  8. Architecture and spatial organization in a triple-species bacterial biofilm synergistically degrading the phenylurea herbicide linuron

    DEFF Research Database (Denmark)

    Breugelmans, Philip; Barken, Kim Bundvig; Tolker-Nielsen, Tim

    2008-01-01

    influence on the triple-species biofilm architecture. This architecture was dependent on the carbon source supplied, as the biofilm architecture of WDL1 growing on alternative carbon sources was different from that observed under linuron-fed conditions. Linuron-fed triple-species consortium biofilms....... These observations indicate that the spatial organization in the linuron-fed consortium biofilm reflected the metabolic interactions within the consortium....

  9. Statistical inference

    CERN Document Server

    Rohatgi, Vijay K

    2003-01-01

    Unified treatment of probability and statistics examines and analyzes the relationship between the two fields, exploring inferential issues. Numerous problems, examples, and diagrams--some with solutions--plus clear-cut, highlighted summaries of results. Advanced undergraduate to graduate level. Contents: 1. Introduction. 2. Probability Model. 3. Probability Distributions. 4. Introduction to Statistical Inference. 5. More on Mathematical Expectation. 6. Some Discrete Models. 7. Some Continuous Models. 8. Functions of Random Variables and Random Vectors. 9. Large-Sample Theory. 10. General Meth

  10. Inferring processes from spatial patterns: the role of directional and non-directional forces in shaping fish larvae distribution in a freshwater lake system.

    Directory of Open Access Journals (Sweden)

    Andrea Bertolo

    Full Text Available Larval dispersal is a crucial factor for fish recruitment. For fishes with relatively small-bodied larvae, drift has the potential to play a more important role than active habitat selection in determining larval dispersal; therefore, we expect small-bodied fish larvae to be poorly associated with habitat characteristics. To test this hypothesis, we used as model yellow perch (Perca flavescens, whose larvae are among the smallest among freshwater temperate fishes. Thus, we analysed the habitat association of yellow perch larvae at multiple spatial scales in a large shallow fluvial lake by explicitly modelling directional (e.g. due to water currents and non-directional (e.g. due to aggregation spatial patterns. This allowed us to indirectly assess the relative roles of drift (directional process and potential habitat choice on larval dispersal. Our results give weak support to the drift hypothesis, whereas yellow perch show a strong habitat association at unexpectedly small sizes, when compared to other systems. We found consistent non-directional patterns in larvae distributions at both broad and medium spatial scales but only few significant directional components. The environmental variables alone (e.g. vegetation generally explained a significant and biologically relevant fraction of the variation in fish larvae distribution data. These results suggest that (i drift plays a minor role in this shallow system, (ii larvae display spatial patterns that only partially covary with environmental variables, and (iii larvae are associated to specific habitats. By suggesting that habitat association potentially includes an active choice component for yellow perch larvae, our results shed new light on the ecology of freshwater fish larvae and should help in building more realistic recruitment models.

  11. Groundwater-related Land Deformation over the Mega Aquifer System in Saudi Arabia: Inferences from InSAR, GRACE, Earthquake records, Field, and Spatial Data Analysis.

    Science.gov (United States)

    Othman, A.; Sultan, M.; Becker, R.; Sefry, S.; Alharbi, T.; Alharbi, H.; Gebremichael, E.

    2017-12-01

    Land deformational features (subsidence, and earth fissures, etc.) are being reported from many locations over the Lower Mega Aquifer System (LMAS) in the central and northern parts of Saudi Arabia. We applied an integrated approach (remote sensing, geodesy, GIS, geology, hydrogeology, and geotechnical) to identify nature, intensity, spatial distribution, and factors controlling the observed deformation. A three-fold approach was adopted to accomplish the following: (1) investigate, identify, and verify the land deformation through fieldwork; (2) assess the spatial and temporal distribution of land deformation and quantify deformation rates using Interferometric Synthetic Aperture Radar (InSAR) and Persistent Scatterer Interferometry (PSI) methods (period: 2003 to 2012); (3) generate a GIS database to host all relevant data and derived products (remote sensing, geology, geotechnical, GPS, groundwater extraction rates, and water levels, etc.) and to correlate these spatial and temporal datasets in search of causal effects. The following observations are consistent with deformational features being caused by excessive groundwater extraction: (1) distribution of deformational features correlated spatially and temporally with increased agricultural development and groundwater extraction, and with the decline in groundwater levels and storage; (2) earthquake events (1.5 - 5.5 M) increased from one event at the beginning of the agricultural development program in 1980 (average annual extraction [ANE]: 1-2 km³/yr), to 13 events per year between 1995 to 2005, the decade that witnessed the largest expansion in groundwater extraction (ANE: >6.4 km³) and land reclamation using groundwater resources; and (3) earthquake epicenters and the deformation sites are found largely within areas bound by the Kahf fault system suggesting that faults play a key role in the deformation phenomenon. Findings from the PSI investigation revealed high, yet irregularly distributed, subsidence

  12. Spatially variable stage-driven groundwater-surface water interaction inferred from time-frequency analysis of distributed temperature sensing data

    Science.gov (United States)

    Mwakanyamale, Kisa; Slater, Lee; Day-Lewis, Frederick D.; Elwaseif, Mehrez; Johnson, Carole D.

    2012-01-01

    Characterization of groundwater-surface water exchange is essential for improving understanding of contaminant transport between aquifers and rivers. Fiber-optic distributed temperature sensing (FODTS) provides rich spatiotemporal datasets for quantitative and qualitative analysis of groundwater-surface water exchange. We demonstrate how time-frequency analysis of FODTS and synchronous river stage time series from the Columbia River adjacent to the Hanford 300-Area, Richland, Washington, provides spatial information on the strength of stage-driven exchange of uranium contaminated groundwater in response to subsurface heterogeneity. Although used in previous studies, the stage-temperature correlation coefficient proved an unreliable indicator of the stage-driven forcing on groundwater discharge in the presence of other factors influencing river water temperature. In contrast, S-transform analysis of the stage and FODTS data definitively identifies the spatial distribution of discharge zones and provided information on the dominant forcing periods (≥2 d) of the complex dam operations driving stage fluctuations and hence groundwater-surface water exchange at the 300-Area.

  13. Congruence between morphological and molecular markers inferred from the analysis of the intra-morphotype genetic diversity and the spatial structure of Oxalis tuberosa Mol.

    Science.gov (United States)

    Pissard, Audrey; Arbizu, Carlos; Ghislain, Marc; Faux, Anne-Michèle; Paulet, Sébastien; Bertin, Pierre

    2008-01-01

    Oxalis tuberosa is an important crop cultivated in the highest Andean zones. A germplasm collection is maintained ex situ by CIP, which has developed a morphological markers system to classify the accessions into morphotypes, i.e. groups of morphologically identical accessions. However, their genetic uniformity is currently unknown. The ISSR technique was used in two experiments to determine the relationships between both morphological and molecular markers systems. The intra-morphotype genetic diversity, the spatial structures of the diversity and the congruence between both markers systems were determined. In the first experience, 44 accessions representing five morphotypes, clearly distinct from each other, were analyzed. At the molecular level, the accessions exactly clustered according to their morphotypes. However, a genetic variability was observed inside each morphotype. In the second experiment, 34 accessions gradually differing from each other on morphological base were analyzed. The morphological clustering showed no geographical structure. On the opposite, the molecular analysis showed that the genetic structure was slightly related to the collection site. The correlation between both markers systems was weak but significant. The lack of perfect congruence between morphological and molecular data suggests that the morphological system may be useful for the morphotypes management but is not appropriate to study the genetic structure of the oca. The spatial structure of the genetic diversity can be related to the evolution of the species and the discordance between the morphological and molecular structures may result from similar selection pressures at different places leading to similar forms with a different genetic background.

  14. Spatial and temporal variability in MLT turbulence inferred from in situ and ground-based observations during the WADIS-1 sounding rocket campaign

    Directory of Open Access Journals (Sweden)

    B. Strelnikov

    2017-04-01

    Full Text Available In summer 2013 the WADIS-1 sounding rocket campaign was conducted at the Andøya Space Center (ACS in northern Norway (69° N, 16° E. Among other things, it addressed the question of the variability in mesosphere/lower thermosphere (MLT turbulence, both in time and space. A unique feature of the WADIS project was multi-point turbulence sounding applying different measurement techniques including rocket-borne ionization gauges, VHF MAARSY radar, and VHF EISCAT radar near Tromsø. This allowed for horizontal variability to be observed in the turbulence field in the MLT at scales from a few to 100 km. We found that the turbulence dissipation rate, ε varied in space in a wavelike manner both horizontally and in the vertical direction. This wavelike modulation reveals the same vertical wavelengths as those seen in gravity waves. We also found that the vertical mean value of radar observations of ε agrees reasonably with rocket-borne measurements. In this way defined 〈εradar〉 value reveals clear tidal modulation and results in variation by up to 2 orders of magnitude with periods of 24 h. The 〈εradar〉 value also shows 12 h and shorter (1 to a few hours modulations resulting in one decade of variation in 〈εradar〉 magnitude. The 24 h modulation appeared to be in phase with tidal change of horizontal wind observed by SAURA-MF radar. Such wavelike and, in particular, tidal modulation of the turbulence dissipation field in the MLT region inferred from our analysis is a new finding of this work.

  15. AIRS-Observed Interrelationships of Anomaly Time-Series of Moist Process-Related Parameters and Inferred Feedback Values on Various Spatial Scales

    Science.gov (United States)

    Molnar, Gyula I.; Susskind, Joel; Iredell, Lena

    2011-01-01

    In the beginning, a good measure of a GMCs performance was their ability to simulate the observed mean seasonal cycle. That is, a reasonable simulation of the means (i.e., small biases) and standard deviations of TODAY?S climate would suffice. Here, we argue that coupled GCM (CG CM for short) simulations of FUTURE climates should be evaluated in much more detail, both spatially and temporally. Arguably, it is not the bias, but rather the reliability of the model-generated anomaly time-series, even down to the [C]GCM grid-scale, which really matter. This statement is underlined by the social need to address potential REGIONAL climate variability, and climate drifts/changes in a manner suitable for policy decisions.

  16. The Temporal and Spatial Variability of the Confined Aquifer Head and Storage Properties in the San Luis Valley, Colorado Inferred From Multiple InSAR Missions

    Science.gov (United States)

    Chen, Jingyi; Knight, Rosemary; Zebker, Howard A.

    2017-11-01

    Interferometric Synthetic Aperture Radar (InSAR) data from multiple satellite missions were combined to study the temporal and spatial variability of head and storage properties in a confined aquifer system on a decadal time scale. The area of study was a 4,500 km2 agricultural basin in the San Luis Valley (SLV), Colorado. We had available previous analyses of C-band ERS-1/2 data from June 1992 to November 2000, and L-band ALOS PALSAR data from October 2009 to March 2011. We used C-band Envisat data to fill in the time period from November 2006 to July 2010. In processing the Envisat data, we successfully employed a phase interpolation between persistent scatterer pixels to reduce the impact of vegetation decorrelation, which can significantly reduce the quality of C-band InSAR data over agricultural basins. In comparing the results from the L-band ALOS data and C-band Envisat data in a 10 month overlapping time period, we found that the shorter wavelength of C-band InSAR allowed us to preserve small deformation signals that were not detectable using L-band ALOS data. A significant result was the finding that the elastic storage properties of the SLV confined aquifer system remained stable over the 20 year time period and vary slowly in space, allowing us to combine InSAR data acquired from multiple missions to fill the temporal and spatial gaps in well data. The InSAR estimated head levels were validated with well measurements, which indicate little permanent water-storage loss over the study time period in the SLV.

  17. Spatial Variability in Column CO2 Inferred from High Resolution GEOS-5 Global Model Simulations: Implications for Remote Sensing and Inversions

    Science.gov (United States)

    Ott, L.; Putman, B.; Collatz, J.; Gregg, W.

    2012-01-01

    concepts to create realistic pseudo-datasets. Pseudo-data are averaged over coarse model grid cell areas to better understand the ability of measurements to characterize CO2 distributions and spatial gradients on both short (daily to weekly) and long (monthly to seasonal) time scales

  18. Interactive Instruction in Bayesian Inference

    DEFF Research Database (Denmark)

    Khan, Azam; Breslav, Simon; Hornbæk, Kasper

    2018-01-01

    An instructional approach is presented to improve human performance in solving Bayesian inference problems. Starting from the original text of the classic Mammography Problem, the textual expression is modified and visualizations are added according to Mayer’s principles of instruction. These pri......An instructional approach is presented to improve human performance in solving Bayesian inference problems. Starting from the original text of the classic Mammography Problem, the textual expression is modified and visualizations are added according to Mayer’s principles of instruction....... These principles concern coherence, personalization, signaling, segmenting, multimedia, spatial contiguity, and pretraining. Principles of self-explanation and interactivity are also applied. Four experiments on the Mammography Problem showed that these principles help participants answer the questions...... that an instructional approach to improving human performance in Bayesian inference is a promising direction....

  19. Meta-learning framework applied in bioinformatics inference system design.

    Science.gov (United States)

    Arredondo, Tomás; Ormazábal, Wladimir

    2015-01-01

    This paper describes a meta-learner inference system development framework which is applied and tested in the implementation of bioinformatic inference systems. These inference systems are used for the systematic classification of the best candidates for inclusion in bacterial metabolic pathway maps. This meta-learner-based approach utilises a workflow where the user provides feedback with final classification decisions which are stored in conjunction with analysed genetic sequences for periodic inference system training. The inference systems were trained and tested with three different data sets related to the bacterial degradation of aromatic compounds. The analysis of the meta-learner-based framework involved contrasting several different optimisation methods with various different parameters. The obtained inference systems were also contrasted with other standard classification methods with accurate prediction capabilities observed.

  20. Models and Inference for Multivariate Spatial Extremes

    KAUST Repository

    Vettori, Sabrina

    2017-01-01

    The development of flexible and interpretable statistical methods is necessary in order to provide appropriate risk assessment measures for extreme events and natural disasters. In this thesis, we address this challenge by contributing

  1. Bacterial meningitis

    NARCIS (Netherlands)

    Roos, Karen L.; van de Beek, Diederik

    2010-01-01

    Bacterial meningitis is a neurological emergency. Empiric antimicrobial and adjunctive therapy should be initiated as soon as a single set of blood cultures has been obtained. Clinical signs suggestive of bacterial meningitis include fever, headache, meningismus, vomiting, photophobia, and an

  2. SEMANTIC PATCH INFERENCE

    DEFF Research Database (Denmark)

    Andersen, Jesper

    2009-01-01

    Collateral evolution the problem of updating several library-using programs in response to API changes in the used library. In this dissertation we address the issue of understanding collateral evolutions by automatically inferring a high-level specification of the changes evident in a given set ...... specifications inferred by spdiff in Linux are shown. We find that the inferred specifications concisely capture the actual collateral evolution performed in the examples....

  3. Likelihood-based inference for clustered line transect data

    DEFF Research Database (Denmark)

    Waagepetersen, Rasmus; Schweder, Tore

    2006-01-01

    The uncertainty in estimation of spatial animal density from line transect surveys depends on the degree of spatial clustering in the animal population. To quantify the clustering we model line transect data as independent thinnings of spatial shot-noise Cox processes. Likelihood-based inference...

  4. Likelihood-based inference for clustered line transect data

    DEFF Research Database (Denmark)

    Waagepetersen, Rasmus Plenge; Schweder, Tore

    The uncertainty in estimation of spatial animal density from line transect surveys depends on the degree of spatial clustering in the animal population. To quantify the clustering we model line transect data as independent thinnings of spatial shot-noise Cox processes. Likelihood-based inference...

  5. Inference in `poor` languages

    Energy Technology Data Exchange (ETDEWEB)

    Petrov, S.

    1996-10-01

    Languages with a solvable implication problem but without complete and consistent systems of inference rules (`poor` languages) are considered. The problem of existence of finite complete and consistent inference rule system for a ``poor`` language is stated independently of the language or rules syntax. Several properties of the problem arc proved. An application of results to the language of join dependencies is given.

  6. Bayesian statistical inference

    Directory of Open Access Journals (Sweden)

    Bruno De Finetti

    2017-04-01

    Full Text Available This work was translated into English and published in the volume: Bruno De Finetti, Induction and Probability, Biblioteca di Statistica, eds. P. Monari, D. Cocchi, Clueb, Bologna, 1993.Bayesian statistical Inference is one of the last fundamental philosophical papers in which we can find the essential De Finetti's approach to the statistical inference.

  7. Geometric statistical inference

    International Nuclear Information System (INIS)

    Periwal, Vipul

    1999-01-01

    A reparametrization-covariant formulation of the inverse problem of probability is explicitly solved for finite sample sizes. The inferred distribution is explicitly continuous for finite sample size. A geometric solution of the statistical inference problem in higher dimensions is outlined

  8. Practical Bayesian Inference

    Science.gov (United States)

    Bailer-Jones, Coryn A. L.

    2017-04-01

    Preface; 1. Probability basics; 2. Estimation and uncertainty; 3. Statistical models and inference; 4. Linear models, least squares, and maximum likelihood; 5. Parameter estimation: single parameter; 6. Parameter estimation: multiple parameters; 7. Approximating distributions; 8. Monte Carlo methods for inference; 9. Parameter estimation: Markov chain Monte Carlo; 10. Frequentist hypothesis testing; 11. Model comparison; 12. Dealing with more complicated problems; References; Index.

  9. Knowledge and inference

    CERN Document Server

    Nagao, Makoto

    1990-01-01

    Knowledge and Inference discusses an important problem for software systems: How do we treat knowledge and ideas on a computer and how do we use inference to solve problems on a computer? The book talks about the problems of knowledge and inference for the purpose of merging artificial intelligence and library science. The book begins by clarifying the concept of """"knowledge"""" from many points of view, followed by a chapter on the current state of library science and the place of artificial intelligence in library science. Subsequent chapters cover central topics in the artificial intellig

  10. Logical inference and evaluation

    International Nuclear Information System (INIS)

    Perey, F.G.

    1981-01-01

    Most methodologies of evaluation currently used are based upon the theory of statistical inference. It is generally perceived that this theory is not capable of dealing satisfactorily with what are called systematic errors. Theories of logical inference should be capable of treating all of the information available, including that not involving frequency data. A theory of logical inference is presented as an extension of deductive logic via the concept of plausibility and the application of group theory. Some conclusions, based upon the application of this theory to evaluation of data, are also given

  11. Probability and Statistical Inference

    OpenAIRE

    Prosper, Harrison B.

    2006-01-01

    These lectures introduce key concepts in probability and statistical inference at a level suitable for graduate students in particle physics. Our goal is to paint as vivid a picture as possible of the concepts covered.

  12. On quantum statistical inference

    NARCIS (Netherlands)

    Barndorff-Nielsen, O.E.; Gill, R.D.; Jupp, P.E.

    2003-01-01

    Interest in problems of statistical inference connected to measurements of quantum systems has recently increased substantially, in step with dramatic new developments in experimental techniques for studying small quantum systems. Furthermore, developments in the theory of quantum measurements have

  13. INFERENCE BUILDING BLOCKS

    Science.gov (United States)

    2018-02-15

    expressed a variety of inference techniques on discrete and continuous distributions: exact inference, importance sampling, Metropolis-Hastings (MH...without redoing any math or rewriting any code. And although our main goal is composable reuse, our performance is also good because we can use...control paths. • The Hakaru language can express mixtures of discrete and continuous distributions, but the current disintegration transformation

  14. Introductory statistical inference

    CERN Document Server

    Mukhopadhyay, Nitis

    2014-01-01

    This gracefully organized text reveals the rigorous theory of probability and statistical inference in the style of a tutorial, using worked examples, exercises, figures, tables, and computer simulations to develop and illustrate concepts. Drills and boxed summaries emphasize and reinforce important ideas and special techniques.Beginning with a review of the basic concepts and methods in probability theory, moments, and moment generating functions, the author moves to more intricate topics. Introductory Statistical Inference studies multivariate random variables, exponential families of dist

  15. Bacterial prostatitis.

    Science.gov (United States)

    Gill, Bradley C; Shoskes, Daniel A

    2016-02-01

    The review provides the infectious disease community with a urologic perspective on bacterial prostatitis. Specifically, the article briefly reviews the categorization of prostatitis by type and provides a distillation of new findings published on bacterial prostatitis over the past year. It also highlights key points from the established literature. Cross-sectional prostate imaging is becoming more common and may lead to more incidental diagnoses of acute bacterial prostatitis. As drug resistance remains problematic in this condition, the reemergence of older antibiotics such as fosfomycin, has proven beneficial. With regard to chronic bacterial prostatitis, no clear clinical risk factors emerged in a large epidemiological study. However, bacterial biofilm formation has been associated with more severe cases. Surgery has a limited role in bacterial prostatitis and should be reserved for draining of a prostatic abscess or the removal of infected prostatic stones. Prostatitis remains a common and bothersome clinical condition. Antibiotic therapy remains the basis of treatment for both acute and chronic bacterial prostatitis. Further research into improving prostatitis treatment is indicated.

  16. The bacterial sequential Markov coalescent

    OpenAIRE

    De Maio, N; Wilson, DJ

    2017-01-01

    Bacteria can exchange and acquire new genetic material from other organisms directly and via the environment. This process, known as bacterial recombination, has a strong impact on the evolution of bacteria, for example leading to the spread of antibiotic resistance across clades and species, and to the avoidance of clonal interference. Recombination hinders phylogenetic and transmission inference because it creates patterns of substitutions that are not consistent with the hypothesis of a si...

  17. Bacterial Vaginosis

    Science.gov (United States)

    ... Archive STDs Home Page Bacterial Vaginosis (BV) Chlamydia Gonorrhea Genital Herpes Hepatitis HIV/AIDS & STDs Human Papillomavirus ( ... of getting other STDs, such as chlamydia and gonorrhea . These bacteria can sometimes cause pelvic inflammatory disease ( ...

  18. Subjective randomness as statistical inference.

    Science.gov (United States)

    Griffiths, Thomas L; Daniels, Dylan; Austerweil, Joseph L; Tenenbaum, Joshua B

    2018-06-01

    Some events seem more random than others. For example, when tossing a coin, a sequence of eight heads in a row does not seem very random. Where do these intuitions about randomness come from? We argue that subjective randomness can be understood as the result of a statistical inference assessing the evidence that an event provides for having been produced by a random generating process. We show how this account provides a link to previous work relating randomness to algorithmic complexity, in which random events are those that cannot be described by short computer programs. Algorithmic complexity is both incomputable and too general to capture the regularities that people can recognize, but viewing randomness as statistical inference provides two paths to addressing these problems: considering regularities generated by simpler computing machines, and restricting the set of probability distributions that characterize regularity. Building on previous work exploring these different routes to a more restricted notion of randomness, we define strong quantitative models of human randomness judgments that apply not just to binary sequences - which have been the focus of much of the previous work on subjective randomness - but also to binary matrices and spatial clustering. Copyright © 2018 Elsevier Inc. All rights reserved.

  19. Type Inference with Inequalities

    DEFF Research Database (Denmark)

    Schwartzbach, Michael Ignatieff

    1991-01-01

    of (monotonic) inequalities on the types of variables and expressions. A general result about systems of inequalities over semilattices yields a solvable form. We distinguish between deciding typability (the existence of solutions) and type inference (the computation of a minimal solution). In our case, both......Type inference can be phrased as constraint-solving over types. We consider an implicitly typed language equipped with recursive types, multiple inheritance, 1st order parametric polymorphism, and assignments. Type correctness is expressed as satisfiability of a possibly infinite collection...

  20. Prevalence of antibiotic resistance genes in bacterial communities associated with Cladophora glomerata mats along the nearshore of Lake Ontario.

    Science.gov (United States)

    Ibsen, Michael; Fernando, Dinesh M; Kumar, Ayush; Kirkwood, Andrea E

    2017-05-01

    The alga Cladophora glomerata can erupt in nuisance blooms throughout the lower Great Lakes. Since bacterial abundance increases with the emergence and decay of Cladophora, we investigated the prevalence of antibiotic resistance (ABR) in Cladophora-associated bacterial communities up-gradient and down-gradient from a large sewage treatment plant (STP) on Lake Ontario. Although STPs are well-known sources of ABR, we also expected detectable ABR from up-gradient wetland communities, since they receive surface run-off from urban and agricultural sources. Statistically significant differences in aquatic bacterial abundance and ABR were found between down-gradient beach samples and up-gradient coastal wetland samples (ANOVA, Holm-Sidak test, p Cladophora sampled near the STP had the highest bacterial densities overall, including on ampicillin- and vancomycin-treated plates. However, quantitative polymerase chain reaction analysis of the ABR genes ampC, tetA, tetB, and vanA from environmental communities showed a different pattern. Some of the highest ABR gene levels occurred at the 2 coastal wetland sites (vanA). Overall, bacterial ABR profiles from environmental samples were distinguishable between living and decaying Cladophora, inferring that Cladophora may control bacterial ABR depending on its life-cycle stage. Our results also show how spatially and temporally dynamic ABR is in nearshore aquatic bacteria, which warrants further research.

  1. Inference as Prediction

    Science.gov (United States)

    Watson, Jane

    2007-01-01

    Inference, or decision making, is seen in curriculum documents as the final step in a statistical investigation. For a formal statistical enquiry this may be associated with sophisticated tests involving probability distributions. For young students without the mathematical background to perform such tests, it is still possible to draw informal…

  2. Hybrid Optical Inference Machines

    Science.gov (United States)

    1991-09-27

    with labels. Now, events. a set of facts cal be generated in the dyadic form "u, R 1,2" Eichmann and Caulfield (19] consider the same type of and can...these enceding-schemes. These architectures are-based pri- 19. G. Eichmann and H. J. Caulfield, "Optical Learning (Inference)marily on optical inner

  3. BACTERIAL CONSORTIUM

    Directory of Open Access Journals (Sweden)

    Payel Sarkar

    2013-01-01

    Full Text Available Petroleum aromatic hydrocarbons like benzen e, toluene, ethyl benzene and xylene, together known as BTEX, has almost the same chemical structure. These aromatic hydrocarbons are released as pollutants in th e environment. This work was taken up to develop a solvent tolerant bacterial cons ortium that could degrade BTEX compounds as they all share a common chemical structure. We have isolated almost 60 different types of bacterial strains from different petroleum contaminated sites. Of these 60 bacterial strains almost 20 microorganisms were screene d on the basis of capability to tolerate high concentration of BTEX. Ten differe nt consortia were prepared and the compatibility of the bacterial strains within the consortia was checked by gram staining and BTEX tolerance level. Four successful mi crobial consortia were selected in which all the bacterial strains concomitantly grew in presence of high concentration of BTEX (10% of toluene, 10% of benzene 5% ethyl benzene and 1% xylene. Consortium #2 showed the highest growth rate in pr esence of BTEX. Degradation of BTEX by consortium #2 was monitored for 5 days by gradual decrease in the volume of the solvents. The maximum reduction observed wa s 85% in 5 days. Gas chromatography results also reveal that could completely degrade benzene and ethyl benzene within 48 hours. Almost 90% degradation of toluene and xylene in 48 hours was exhibited by consortium #2. It could also tolerate and degrade many industrial solvents such as chloroform, DMSO, acetonitrile having a wide range of log P values (0.03–3.1. Degradation of aromatic hydrocarbon like BTEX by a solvent tolerant bacterial consortium is greatly significant as it could degrade high concentration of pollutants compared to a bacterium and also reduces the time span of degradation.

  4. Inference rule and problem solving

    Energy Technology Data Exchange (ETDEWEB)

    Goto, S

    1982-04-01

    Intelligent information processing signifies an opportunity of having man's intellectual activity executed on the computer, in which inference, in place of ordinary calculation, is used as the basic operational mechanism for such an information processing. Many inference rules are derived from syllogisms in formal logic. The problem of programming this inference function is referred to as a problem solving. Although logically inference and problem-solving are in close relation, the calculation ability of current computers is on a low level for inferring. For clarifying the relation between inference and computers, nonmonotonic logic has been considered. The paper deals with the above topics. 16 references.

  5. Bacterial Adhesion & Blocking Bacterial Adhesion

    DEFF Research Database (Denmark)

    Vejborg, Rebecca Munk

    2008-01-01

    , which influence the transition from a planktonic lifestyle to a sessile lifestyle, have been studied. Protein conditioning film formation was found to influence bacterial adhesion and subsequent biofilm formation considerable, and an aqueous extract of fish muscle tissue was shown to significantly...... tract to the microbial flocs in waste water treatment facilities. Microbial biofilms may however also cause a wide range of industrial and medical problems, and have been implicated in a wide range of persistent infectious diseases, including implantassociated microbial infections. Bacterial adhesion...... is the first committing step in biofilm formation, and has therefore been intensely scrutinized. Much however, still remains elusive. Bacterial adhesion is a highly complex process, which is influenced by a variety of factors. In this thesis, a range of physico-chemical, molecular and environmental parameters...

  6. Bacterial meningitis

    NARCIS (Netherlands)

    Heckenberg, Sebastiaan G. B.; Brouwer, Matthijs C.; van de Beek, Diederik

    2014-01-01

    Bacterial meningitis is a neurologic emergency. Vaccination against common pathogens has decreased the burden of disease. Early diagnosis and rapid initiation of empiric antimicrobial and adjunctive therapy are vital. Therapy should be initiated as soon as blood cultures have been obtained,

  7. Bacterial lipases

    NARCIS (Netherlands)

    Jaeger, Karl-Erich; Ransac, Stéphane; Dijkstra, Bauke W.; Colson, Charles; Heuvel, Margreet van; Misset, Onno

    Many different bacterial species produce lipases which hydrolyze esters of glycerol with preferably long-chain fatty acids. They act at the interface generated by a hydrophobic lipid substrate in a hydrophilic aqueous medium. A characteristic property of lipases is called interfacial activation,

  8. Bacterial stress

    Indian Academy of Sciences (India)

    First page Back Continue Last page Graphics. Bacterial stress. Physicochemical and chemical parameters: temperature, pressure, pH, salt concentration, oxygen, irradiation. Nutritional depravation: nutrient starvation, water shortage. Toxic compounds: Antibiotics, heavy metals, toxins, mutagens. Interactions with other cells: ...

  9. Stochastic processes inference theory

    CERN Document Server

    Rao, Malempati M

    2014-01-01

    This is the revised and enlarged 2nd edition of the authors’ original text, which was intended to be a modest complement to Grenander's fundamental memoir on stochastic processes and related inference theory. The present volume gives a substantial account of regression analysis, both for stochastic processes and measures, and includes recent material on Ridge regression with some unexpected applications, for example in econometrics. The first three chapters can be used for a quarter or semester graduate course on inference on stochastic processes. The remaining chapters provide more advanced material on stochastic analysis suitable for graduate seminars and discussions, leading to dissertation or research work. In general, the book will be of interest to researchers in probability theory, mathematical statistics and electrical and information theory.

  10. Making Type Inference Practical

    DEFF Research Database (Denmark)

    Schwartzbach, Michael Ignatieff; Oxhøj, Nicholas; Palsberg, Jens

    1992-01-01

    We present the implementation of a type inference algorithm for untyped object-oriented programs with inheritance, assignments, and late binding. The algorithm significantly improves our previous one, presented at OOPSLA'91, since it can handle collection classes, such as List, in a useful way. Abo......, the complexity has been dramatically improved, from exponential time to low polynomial time. The implementation uses the techniques of incremental graph construction and constraint template instantiation to avoid representing intermediate results, doing superfluous work, and recomputing type information....... Experiments indicate that the implementation type checks as much as 100 lines pr. second. This results in a mature product, on which a number of tools can be based, for example a safety tool, an image compression tool, a code optimization tool, and an annotation tool. This may make type inference for object...

  11. Russell and Humean Inferences

    Directory of Open Access Journals (Sweden)

    João Paulo Monteiro

    2001-12-01

    Full Text Available Russell's The Problems of Philosophy tries to establish a new theory of induction, at the same time that Hume is there accused of an irrational/ scepticism about induction". But a careful analysis of the theory of knowledge explicitly acknowledged by Hume reveals that, contrary to the standard interpretation in the XXth century, possibly influenced by Russell, Hume deals exclusively with causal inference (which he never classifies as "causal induction", although now we are entitled to do so, never with inductive inference in general, mainly generalizations about sensible qualities of objects ( whether, e.g., "all crows are black" or not is not among Hume's concerns. Russell's theories are thus only false alternatives to Hume's, in (1912 or in his (1948.

  12. Causal inference in econometrics

    CERN Document Server

    Kreinovich, Vladik; Sriboonchitta, Songsak

    2016-01-01

    This book is devoted to the analysis of causal inference which is one of the most difficult tasks in data analysis: when two phenomena are observed to be related, it is often difficult to decide whether one of them causally influences the other one, or whether these two phenomena have a common cause. This analysis is the main focus of this volume. To get a good understanding of the causal inference, it is important to have models of economic phenomena which are as accurate as possible. Because of this need, this volume also contains papers that use non-traditional economic models, such as fuzzy models and models obtained by using neural networks and data mining techniques. It also contains papers that apply different econometric models to analyze real-life economic dependencies.

  13. Active inference and learning.

    Science.gov (United States)

    Friston, Karl; FitzGerald, Thomas; Rigoli, Francesco; Schwartenbeck, Philipp; O Doherty, John; Pezzulo, Giovanni

    2016-09-01

    This paper offers an active inference account of choice behaviour and learning. It focuses on the distinction between goal-directed and habitual behaviour and how they contextualise each other. We show that habits emerge naturally (and autodidactically) from sequential policy optimisation when agents are equipped with state-action policies. In active inference, behaviour has explorative (epistemic) and exploitative (pragmatic) aspects that are sensitive to ambiguity and risk respectively, where epistemic (ambiguity-resolving) behaviour enables pragmatic (reward-seeking) behaviour and the subsequent emergence of habits. Although goal-directed and habitual policies are usually associated with model-based and model-free schemes, we find the more important distinction is between belief-free and belief-based schemes. The underlying (variational) belief updating provides a comprehensive (if metaphorical) process theory for several phenomena, including the transfer of dopamine responses, reversal learning, habit formation and devaluation. Finally, we show that active inference reduces to a classical (Bellman) scheme, in the absence of ambiguity. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

  14. Learning Convex Inference of Marginals

    OpenAIRE

    Domke, Justin

    2012-01-01

    Graphical models trained using maximum likelihood are a common tool for probabilistic inference of marginal distributions. However, this approach suffers difficulties when either the inference process or the model is approximate. In this paper, the inference process is first defined to be the minimization of a convex function, inspired by free energy approximations. Learning is then done directly in terms of the performance of the inference process at univariate marginal prediction. The main ...

  15. Probabilistic inductive inference: a survey

    OpenAIRE

    Ambainis, Andris

    2001-01-01

    Inductive inference is a recursion-theoretic theory of learning, first developed by E. M. Gold (1967). This paper surveys developments in probabilistic inductive inference. We mainly focus on finite inference of recursive functions, since this simple paradigm has produced the most interesting (and most complex) results.

  16. Multimodel inference and adaptive management

    Science.gov (United States)

    Rehme, S.E.; Powell, L.A.; Allen, Craig R.

    2011-01-01

    Ecology is an inherently complex science coping with correlated variables, nonlinear interactions and multiple scales of pattern and process, making it difficult for experiments to result in clear, strong inference. Natural resource managers, policy makers, and stakeholders rely on science to provide timely and accurate management recommendations. However, the time necessary to untangle the complexities of interactions within ecosystems is often far greater than the time available to make management decisions. One method of coping with this problem is multimodel inference. Multimodel inference assesses uncertainty by calculating likelihoods among multiple competing hypotheses, but multimodel inference results are often equivocal. Despite this, there may be pressure for ecologists to provide management recommendations regardless of the strength of their study’s inference. We reviewed papers in the Journal of Wildlife Management (JWM) and the journal Conservation Biology (CB) to quantify the prevalence of multimodel inference approaches, the resulting inference (weak versus strong), and how authors dealt with the uncertainty. Thirty-eight percent and 14%, respectively, of articles in the JWM and CB used multimodel inference approaches. Strong inference was rarely observed, with only 7% of JWM and 20% of CB articles resulting in strong inference. We found the majority of weak inference papers in both journals (59%) gave specific management recommendations. Model selection uncertainty was ignored in most recommendations for management. We suggest that adaptive management is an ideal method to resolve uncertainty when research results in weak inference.

  17. [Bacterial vaginosis].

    Science.gov (United States)

    Romero Herrero, Daniel; Andreu Domingo, Antonia

    2016-07-01

    Bacterial vaginosis (BV) is the main cause of vaginal dysbacteriosis in the women during the reproductive age. It is an entity in which many studies have focused for years and which is still open for discussion topics. This is due to the diversity of microorganisms that cause it and therefore, its difficult treatment. Bacterial vaginosis is probably the result of vaginal colonization by complex bacterial communities, many of them non-cultivable and with interdependent metabolism where anaerobic populations most likely play an important role in its pathogenesis. The main symptoms are an increase of vaginal discharge and the unpleasant smell of it. It can lead to serious consequences for women, such as an increased risk of contracting sexually transmitted infections including human immunodeficiency virus and upper genital tract and pregnancy complications. Gram stain is the gold standard for microbiological diagnosis of BV, but can also be diagnosed using the Amsel clinical criteria. It should not be considered a sexually transmitted disease but it is highly related to sex. Recurrence is the main problem of medical treatment. Apart from BV, there are other dysbacteriosis less characterized like aerobic vaginitis of which further studies are coming slowly but are achieving more attention and consensus among specialists. Copyright © 2016 Elsevier España, S.L.U. All rights reserved.

  18. The Bacterial Sequential Markov Coalescent.

    Science.gov (United States)

    De Maio, Nicola; Wilson, Daniel J

    2017-05-01

    Bacteria can exchange and acquire new genetic material from other organisms directly and via the environment. This process, known as bacterial recombination, has a strong impact on the evolution of bacteria, for example, leading to the spread of antibiotic resistance across clades and species, and to the avoidance of clonal interference. Recombination hinders phylogenetic and transmission inference because it creates patterns of substitutions (homoplasies) inconsistent with the hypothesis of a single evolutionary tree. Bacterial recombination is typically modeled as statistically akin to gene conversion in eukaryotes, i.e. , using the coalescent with gene conversion (CGC). However, this model can be very computationally demanding as it needs to account for the correlations of evolutionary histories of even distant loci. So, with the increasing popularity of whole genome sequencing, the need has emerged for a faster approach to model and simulate bacterial genome evolution. We present a new model that approximates the coalescent with gene conversion: the bacterial sequential Markov coalescent (BSMC). Our approach is based on a similar idea to the sequential Markov coalescent (SMC)-an approximation of the coalescent with crossover recombination. However, bacterial recombination poses hurdles to a sequential Markov approximation, as it leads to strong correlations and linkage disequilibrium across very distant sites in the genome. Our BSMC overcomes these difficulties, and shows a considerable reduction in computational demand compared to the exact CGC, and very similar patterns in simulated data. We implemented our BSMC model within new simulation software FastSimBac. In addition to the decreased computational demand compared to previous bacterial genome evolution simulators, FastSimBac provides more general options for evolutionary scenarios, allowing population structure with migration, speciation, population size changes, and recombination hotspots. FastSimBac is

  19. The NIFTY way of Bayesian signal inference

    International Nuclear Information System (INIS)

    Selig, Marco

    2014-01-01

    We introduce NIFTY, 'Numerical Information Field Theory', a software package for the development of Bayesian signal inference algorithms that operate independently from any underlying spatial grid and its resolution. A large number of Bayesian and Maximum Entropy methods for 1D signal reconstruction, 2D imaging, as well as 3D tomography, appear formally similar, but one often finds individualized implementations that are neither flexible nor easily transferable. Signal inference in the framework of NIFTY can be done in an abstract way, such that algorithms, prototyped in 1D, can be applied to real world problems in higher-dimensional settings. NIFTY as a versatile library is applicable and already has been applied in 1D, 2D, 3D and spherical settings. A recent application is the D 3 PO algorithm targeting the non-trivial task of denoising, deconvolving, and decomposing photon observations in high energy astronomy

  20. The NIFTy way of Bayesian signal inference

    Science.gov (United States)

    Selig, Marco

    2014-12-01

    We introduce NIFTy, "Numerical Information Field Theory", a software package for the development of Bayesian signal inference algorithms that operate independently from any underlying spatial grid and its resolution. A large number of Bayesian and Maximum Entropy methods for 1D signal reconstruction, 2D imaging, as well as 3D tomography, appear formally similar, but one often finds individualized implementations that are neither flexible nor easily transferable. Signal inference in the framework of NIFTy can be done in an abstract way, such that algorithms, prototyped in 1D, can be applied to real world problems in higher-dimensional settings. NIFTy as a versatile library is applicable and already has been applied in 1D, 2D, 3D and spherical settings. A recent application is the D3PO algorithm targeting the non-trivial task of denoising, deconvolving, and decomposing photon observations in high energy astronomy.

  1. Nonparametric statistical inference

    CERN Document Server

    Gibbons, Jean Dickinson

    2010-01-01

    Overall, this remains a very fine book suitable for a graduate-level course in nonparametric statistics. I recommend it for all people interested in learning the basic ideas of nonparametric statistical inference.-Eugenia Stoimenova, Journal of Applied Statistics, June 2012… one of the best books available for a graduate (or advanced undergraduate) text for a theory course on nonparametric statistics. … a very well-written and organized book on nonparametric statistics, especially useful and recommended for teachers and graduate students.-Biometrics, 67, September 2011This excellently presente

  2. Emotional inferences by pragmatics

    OpenAIRE

    Iza-Miqueleiz, Mauricio

    2017-01-01

    It has for long been taken for granted that, along the course of reading a text, world knowledge is often required in order to establish coherent links between sentences (McKoon & Ratcliff 1992, Iza & Ezquerro 2000). The content grasped from a text turns out to be strongly dependent upon the reader’s additional knowledge that allows a coherent interpretation of the text as a whole. The world knowledge directing the inference may be of distinctive nature. Gygax et al. (2007) showed that m...

  3. Generic patch inference

    DEFF Research Database (Denmark)

    Andersen, Jesper; Lawall, Julia

    2010-01-01

    A key issue in maintaining Linux device drivers is the need to keep them up to date with respect to evolutions in Linux internal libraries. Currently, there is little tool support for performing and documenting such changes. In this paper we present a tool, spdiff, that identifies common changes...... developers can use it to extract an abstract representation of the set of changes that others have made. Our experiments on recent changes in Linux show that the inferred generic patches are more concise than the corresponding patches found in commits to the Linux source tree while being safe with respect...

  4. Ancient Biomolecules and Evolutionary Inference.

    Science.gov (United States)

    Cappellini, Enrico; Prohaska, Ana; Racimo, Fernando; Welker, Frido; Pedersen, Mikkel Winther; Allentoft, Morten E; de Barros Damgaard, Peter; Gutenbrunner, Petra; Dunne, Julie; Hammann, Simon; Roffet-Salque, Mélanie; Ilardo, Melissa; Moreno-Mayar, J Víctor; Wang, Yucheng; Sikora, Martin; Vinner, Lasse; Cox, Jürgen; Evershed, Richard P; Willerslev, Eske

    2018-04-25

    Over the last decade, studies of ancient biomolecules-particularly ancient DNA, proteins, and lipids-have revolutionized our understanding of evolutionary history. Though initially fraught with many challenges, the field now stands on firm foundations. Researchers now successfully retrieve nucleotide and amino acid sequences, as well as lipid signatures, from progressively older samples, originating from geographic areas and depositional environments that, until recently, were regarded as hostile to long-term preservation of biomolecules. Sampling frequencies and the spatial and temporal scope of studies have also increased markedly, and with them the size and quality of the data sets generated. This progress has been made possible by continuous technical innovations in analytical methods, enhanced criteria for the selection of ancient samples, integrated experimental methods, and advanced computational approaches. Here, we discuss the history and current state of ancient biomolecule research, its applications to evolutionary inference, and future directions for this young and exciting field. Expected final online publication date for the Annual Review of Biochemistry Volume 87 is June 20, 2018. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

  5. Bacterial mitosis

    DEFF Research Database (Denmark)

    Møller-Jensen, Jakob; Borch, Jonas; Dam, Mette

    2003-01-01

    Bacterial DNA segregation takes place in an active and ordered fashion. In the case of Escherichia coli plasmid R1, the partitioning system (par) separates paired plasmid copies and moves them to opposite cell poles. Here we address the mechanism by which the three components of the R1 par system...... act together to generate the force required for plasmid movement during segregation. ParR protein binds cooperatively to the centromeric parC DNA region, thereby forming a complex that interacts with the filament-forming actin-like ParM protein in an ATP-dependent manner, suggesting that plasmid...

  6. Bacterial Ecology

    DEFF Research Database (Denmark)

    Fenchel, Tom

    2011-01-01

    compounds these must first be undergo extracellular hydrolysis. Bacteria have a great diversity with respect to types of metabolism that far exceeds the metabolic repertoire of eukaryotic organisms. Bacteria play a fundamental role in the biosphere and certain key processes such as, for example......, the production and oxidation of methane, nitrate reduction and fixation of atmospheric nitrogen are exclusively carried out by different groups of bacteria. Some bacterial species – ‘extremophiles’ – thrive in extreme environments in which no eukaryotic organisms can survive with respect to temperature, salinity...... biogeochemical processes are carried exclusively by bacteria. * Bacteria play an important role in all types of habitats including some that cannot support eukaryotic life....

  7. Bacterial Actins.

    Science.gov (United States)

    Izoré, Thierry; van den Ent, Fusinita

    2017-01-01

    A diverse set of protein polymers, structurally related to actin filaments contributes to the organization of bacterial cells as cytomotive or cytoskeletal filaments. This chapter describes actin homologs encoded by bacterial chromosomes. MamK filaments, unique to magnetotactic bacteria, help establishing magnetic biological compasses by interacting with magnetosomes. Magnetosomes are intracellular membrane invaginations containing biomineralized crystals of iron oxide that are positioned by MamK along the long-axis of the cell. FtsA is widespread across bacteria and it is one of the earliest components of the divisome to arrive at midcell, where it anchors the cell division machinery to the membrane. FtsA binds directly to FtsZ filaments and to the membrane through its C-terminus. FtsA shows altered domain architecture when compared to the canonical actin fold. FtsA's subdomain 1C replaces subdomain 1B of other members of the actin family and is located on the opposite side of the molecule. Nevertheless, when FtsA assembles into protofilaments, the protofilament structure is preserved, as subdomain 1C replaces subdomain IB of the following subunit in a canonical actin filament. MreB has an essential role in shape-maintenance of most rod-shaped bacteria. Unusually, MreB filaments assemble from two protofilaments in a flat and antiparallel arrangement. This non-polar architecture implies that both MreB filament ends are structurally identical. MreB filaments bind directly to membranes where they interact with both cytosolic and membrane proteins, thereby forming a key component of the elongasome. MreB filaments in cells are short and dynamic, moving around the long axis of rod-shaped cells, sensing curvature of the membrane and being implicated in peptidoglycan synthesis.

  8. Inferring epidemic network topology from surveillance data.

    Directory of Open Access Journals (Sweden)

    Xiang Wan

    Full Text Available The transmission of infectious diseases can be affected by many or even hidden factors, making it difficult to accurately predict when and where outbreaks may emerge. One approach at the moment is to develop and deploy surveillance systems in an effort to detect outbreaks as timely as possible. This enables policy makers to modify and implement strategies for the control of the transmission. The accumulated surveillance data including temporal, spatial, clinical, and demographic information, can provide valuable information with which to infer the underlying epidemic networks. Such networks can be quite informative and insightful as they characterize how infectious diseases transmit from one location to another. The aim of this work is to develop a computational model that allows inferences to be made regarding epidemic network topology in heterogeneous populations. We apply our model on the surveillance data from the 2009 H1N1 pandemic in Hong Kong. The inferred epidemic network displays significant effect on the propagation of infectious diseases.

  9. Likelihood based inference for partially observed renewal processes

    NARCIS (Netherlands)

    van Lieshout, Maria Nicolette Margaretha

    2016-01-01

    This paper is concerned with inference for renewal processes on the real line that are observed in a broken interval. For such processes, the classic history-based approach cannot be used. Instead, we adapt tools from sequential spatial point process theory to propose a Monte Carlo maximum

  10. Feature Inference Learning and Eyetracking

    Science.gov (United States)

    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…

  11. An Inference Language for Imaging

    DEFF Research Database (Denmark)

    Pedemonte, Stefano; Catana, Ciprian; Van Leemput, Koen

    2014-01-01

    We introduce iLang, a language and software framework for probabilistic inference. The iLang framework enables the definition of directed and undirected probabilistic graphical models and the automated synthesis of high performance inference algorithms for imaging applications. The iLang framewor...

  12. Gauging Variational Inference

    Energy Technology Data Exchange (ETDEWEB)

    Chertkov, Michael [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Ahn, Sungsoo [Korea Advanced Inst. Science and Technology (KAIST), Daejeon (Korea, Republic of); Shin, Jinwoo [Korea Advanced Inst. Science and Technology (KAIST), Daejeon (Korea, Republic of)

    2017-05-25

    Computing partition function is the most important statistical inference task arising in applications of Graphical Models (GM). Since it is computationally intractable, approximate methods have been used to resolve the issue in practice, where meanfield (MF) and belief propagation (BP) are arguably the most popular and successful approaches of a variational type. In this paper, we propose two new variational schemes, coined Gauged-MF (G-MF) and Gauged-BP (G-BP), improving MF and BP, respectively. Both provide lower bounds for the partition function by utilizing the so-called gauge transformation which modifies factors of GM while keeping the partition function invariant. Moreover, we prove that both G-MF and G-BP are exact for GMs with a single loop of a special structure, even though the bare MF and BP perform badly in this case. Our extensive experiments, on complete GMs of relatively small size and on large GM (up-to 300 variables) confirm that the newly proposed algorithms outperform and generalize MF and BP.

  13. Social Inference Through Technology

    Science.gov (United States)

    Oulasvirta, Antti

    Awareness cues are computer-mediated, real-time indicators of people’s undertakings, whereabouts, and intentions. Already in the mid-1970 s, UNIX users could use commands such as “finger” and “talk” to find out who was online and to chat. The small icons in instant messaging (IM) applications that indicate coconversants’ presence in the discussion space are the successors of “finger” output. Similar indicators can be found in online communities, media-sharing services, Internet relay chat (IRC), and location-based messaging applications. But presence and availability indicators are only the tip of the iceberg. Technological progress has enabled richer, more accurate, and more intimate indicators. For example, there are mobile services that allow friends to query and follow each other’s locations. Remote monitoring systems developed for health care allow relatives and doctors to assess the wellbeing of homebound patients (see, e.g., Tang and Venables 2000). But users also utilize cues that have not been deliberately designed for this purpose. For example, online gamers pay attention to other characters’ behavior to infer what the other players are like “in real life.” There is a common denominator underlying these examples: shared activities rely on the technology’s representation of the remote person. The other human being is not physically present but present only through a narrow technological channel.

  14. Bacterial assemblages of the eastern Atlantic Ocean reveal both vertical and latitudinal biogeographic signatures

    Science.gov (United States)

    Friedline, C. J.; Franklin, R. B.; McCallister, S. L.; Rivera, M. C.

    2012-06-01

    Microbial communities are recognized as major drivers of the biogeochemical processes in the oceans. However, the genetic diversity and composition of those communities is poorly understood. The aim of this study is to investigate the composition of bacterial assemblages in three different water layer habitats: surface (2-20 m), deep chlorophyll maximum (DCM; 28-90 m), and deep (100-4600 m) at nine stations along the eastern Atlantic Ocean from 42.8° N to 23.7° S. The sampling of three discrete, predefined habitat types from different depths, Longhurstian provinces, and geographical locations allowed us to investigate whether marine bacterial assemblages show spatial variation and to determine if the observed spatial variation is influenced by current environmental conditions, historical/geographical contingencies, or both. The PCR amplicons of the V6 region of the 16S rRNA from 16 microbial assemblages were pyrosequenced, generating a total of 352 029 sequences; after quality filtering and processing, 257 260 sequences were clustered into 2871 normalized operational taxonomic units (OTU) using a definition of 97% sequence identity. Community ecology statistical analyses demonstrate that the eastern Atlantic Ocean bacterial assemblages are vertically stratified and associated with water layers characterized by unique environmental signals (e.g., temperature, salinity, and nutrients). Genetic compositions of bacterial assemblages from the same water layer are more similar to each other than to assemblages from different water layers. The observed clustering of samples by water layer allows us to conclude that contemporary environments are influencing the observed biogeographic patterns. Moreover, the implementation of a novel Bayesian inference approach that allows a more efficient and explicit use of all the OTU abundance data shows a distance effect suggesting the influence of historical contingencies on the composition of bacterial assemblages. Surface

  15. Bacterial assemblages of the eastern Atlantic Ocean reveal both vertical and latitudinal biogeographic signatures

    Directory of Open Access Journals (Sweden)

    C. J. Friedline

    2012-06-01

    Full Text Available Microbial communities are recognized as major drivers of the biogeochemical processes in the oceans. However, the genetic diversity and composition of those communities is poorly understood. The aim of this study is to investigate the composition of bacterial assemblages in three different water layer habitats: surface (2–20 m, deep chlorophyll maximum (DCM; 28–90 m, and deep (100–4600 m at nine stations along the eastern Atlantic Ocean from 42.8° N to 23.7° S. The sampling of three discrete, predefined habitat types from different depths, Longhurstian provinces, and geographical locations allowed us to investigate whether marine bacterial assemblages show spatial variation and to determine if the observed spatial variation is influenced by current environmental conditions, historical/geographical contingencies, or both. The PCR amplicons of the V6 region of the 16S rRNA from 16 microbial assemblages were pyrosequenced, generating a total of 352 029 sequences; after quality filtering and processing, 257 260 sequences were clustered into 2871 normalized operational taxonomic units (OTU using a definition of 97% sequence identity. Community ecology statistical analyses demonstrate that the eastern Atlantic Ocean bacterial assemblages are vertically stratified and associated with water layers characterized by unique environmental signals (e.g., temperature, salinity, and nutrients. Genetic compositions of bacterial assemblages from the same water layer are more similar to each other than to assemblages from different water layers. The observed clustering of samples by water layer allows us to conclude that contemporary environments are influencing the observed biogeographic patterns. Moreover, the implementation of a novel Bayesian inference approach that allows a more efficient and explicit use of all the OTU abundance data shows a distance effect suggesting the influence of historical contingencies on the composition of bacterial

  16. It's about time - a critique of macroecological inferences concerning plant competition

    DEFF Research Database (Denmark)

    Damgaard, Christian Frølund; Weiner, Jacob

    2017-01-01

    Several macroecological studies have used static spatial data to evaluate plant competition in natural ecosystems and to investigate its role in plant community dynamics and species assembly. The assumptions on which the inferences are based have not been consistent with ecological knowledge....... Inferences about processes, such as competition, from static data are weak. Macroecology will benefit more from dynamic data, even if limited, than from increasingly sophisticated analyses of static spatial patterns....

  17. It's About Time: A Critique of Macroecological Inferences Concerning Plant Competition.

    Science.gov (United States)

    Damgaard, Christian; Weiner, Jacob

    2017-02-01

    Several macroecological studies have used static spatial data to evaluate plant competition in natural ecosystems and to investigate its role in plant community dynamics and species assembly. The assumptions on which the inferences are based have not been consistent with ecological knowledge. Inferences about processes, such as competition, from static data are weak. Macroecology will benefit more from dynamic data, even if limited, than from increasingly sophisticated analyses of static spatial patterns. Copyright © 2016 Elsevier Ltd. All rights reserved.

  18. Optimization methods for logical inference

    CERN Document Server

    Chandru, Vijay

    2011-01-01

    Merging logic and mathematics in deductive inference-an innovative, cutting-edge approach. Optimization methods for logical inference? Absolutely, say Vijay Chandru and John Hooker, two major contributors to this rapidly expanding field. And even though ""solving logical inference problems with optimization methods may seem a bit like eating sauerkraut with chopsticks. . . it is the mathematical structure of a problem that determines whether an optimization model can help solve it, not the context in which the problem occurs."" Presenting powerful, proven optimization techniques for logic in

  19. Barriers to bacterial motility on unsaturated surfaces

    DEFF Research Database (Denmark)

    Dechesne, Arnaud; Smets, Barth F.

    2013-01-01

    Our knowledge of the spatial organization and spatial dynamics of microbial populations in soil at a scale close to that of the microorganisms is scarce. While passive dispersal via water ow or soil biota is probably a major dispersal route, it is reasonable to consider that active dispersal also...... and their isogenic mutants unable to express various type of motility we aimed to quantify the physical limits of bacterial motility. Our results demonstrate how hydration controls bacterial motility under unsaturated conditions. They can form the base of improved biodegradation models that include microbial...

  20. On principles of inductive inference

    OpenAIRE

    Kostecki, Ryszard Paweł

    2011-01-01

    We propose an intersubjective epistemic approach to foundations of probability theory and statistical inference, based on relative entropy and category theory, and aimed to bypass the mathematical and conceptual problems of existing foundational approaches.

  1. Statistical inference via fiducial methods

    OpenAIRE

    Salomé, Diemer

    1998-01-01

    In this thesis the attention is restricted to inductive reasoning using a mathematical probability model. A statistical procedure prescribes, for every theoretically possible set of data, the inference about the unknown of interest. ... Zie: Summary

  2. Statistical inference for stochastic processes

    National Research Council Canada - National Science Library

    Basawa, Ishwar V; Prakasa Rao, B. L. S

    1980-01-01

    The aim of this monograph is to attempt to reduce the gap between theory and applications in the area of stochastic modelling, by directing the interest of future researchers to the inference aspects...

  3. Active inference, communication and hermeneutics.

    Science.gov (United States)

    Friston, Karl J; Frith, Christopher D

    2015-07-01

    Hermeneutics refers to interpretation and translation of text (typically ancient scriptures) but also applies to verbal and non-verbal communication. In a psychological setting it nicely frames the problem of inferring the intended content of a communication. In this paper, we offer a solution to the problem of neural hermeneutics based upon active inference. In active inference, action fulfils predictions about how we will behave (e.g., predicting we will speak). Crucially, these predictions can be used to predict both self and others--during speaking and listening respectively. Active inference mandates the suppression of prediction errors by updating an internal model that generates predictions--both at fast timescales (through perceptual inference) and slower timescales (through perceptual learning). If two agents adopt the same model, then--in principle--they can predict each other and minimise their mutual prediction errors. Heuristically, this ensures they are singing from the same hymn sheet. This paper builds upon recent work on active inference and communication to illustrate perceptual learning using simulated birdsongs. Our focus here is the neural hermeneutics implicit in learning, where communication facilitates long-term changes in generative models that are trying to predict each other. In other words, communication induces perceptual learning and enables others to (literally) change our minds and vice versa. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

  4. Optimal inference with suboptimal models: Addiction and active Bayesian inference

    Science.gov (United States)

    Schwartenbeck, Philipp; FitzGerald, Thomas H.B.; Mathys, Christoph; Dolan, Ray; Wurst, Friedrich; Kronbichler, Martin; Friston, Karl

    2015-01-01

    When casting behaviour as active (Bayesian) inference, optimal inference is defined with respect to an agent’s beliefs – based on its generative model of the world. This contrasts with normative accounts of choice behaviour, in which optimal actions are considered in relation to the true structure of the environment – as opposed to the agent’s beliefs about worldly states (or the task). This distinction shifts an understanding of suboptimal or pathological behaviour away from aberrant inference as such, to understanding the prior beliefs of a subject that cause them to behave less ‘optimally’ than our prior beliefs suggest they should behave. Put simply, suboptimal or pathological behaviour does not speak against understanding behaviour in terms of (Bayes optimal) inference, but rather calls for a more refined understanding of the subject’s generative model upon which their (optimal) Bayesian inference is based. Here, we discuss this fundamental distinction and its implications for understanding optimality, bounded rationality and pathological (choice) behaviour. We illustrate our argument using addictive choice behaviour in a recently described ‘limited offer’ task. Our simulations of pathological choices and addictive behaviour also generate some clear hypotheses, which we hope to pursue in ongoing empirical work. PMID:25561321

  5. Models for inference in dynamic metacommunity systems

    Science.gov (United States)

    Dorazio, Robert M.; Kery, Marc; Royle, J. Andrew; Plattner, Matthias

    2010-01-01

    A variety of processes are thought to be involved in the formation and dynamics of species assemblages. For example, various metacommunity theories are based on differences in the relative contributions of dispersal of species among local communities and interactions of species within local communities. Interestingly, metacommunity theories continue to be advanced without much empirical validation. Part of the problem is that statistical models used to analyze typical survey data either fail to specify ecological processes with sufficient complexity or they fail to account for errors in detection of species during sampling. In this paper, we describe a statistical modeling framework for the analysis of metacommunity dynamics that is based on the idea of adopting a unified approach, multispecies occupancy modeling, for computing inferences about individual species, local communities of species, or the entire metacommunity of species. This approach accounts for errors in detection of species during sampling and also allows different metacommunity paradigms to be specified in terms of species- and location-specific probabilities of occurrence, extinction, and colonization: all of which are estimable. In addition, this approach can be used to address inference problems that arise in conservation ecology, such as predicting temporal and spatial changes in biodiversity for use in making conservation decisions. To illustrate, we estimate changes in species composition associated with the species-specific phenologies of flight patterns of butterflies in Switzerland for the purpose of estimating regional differences in biodiversity.

  6. Bacterial streamers in curved microchannels

    Science.gov (United States)

    Rusconi, Roberto; Lecuyer, Sigolene; Guglielmini, Laura; Stone, Howard

    2009-11-01

    Biofilms, generally identified as microbial communities embedded in a self-produced matrix of extracellular polymeric substances, are involved in a wide variety of health-related problems ranging from implant-associated infections to disease transmissions and dental plaque. The usual picture of these bacterial films is that they grow and develop on surfaces. However, suspended biofilm structures, or streamers, have been found in natural environments (e.g., rivers, acid mines, hydrothermal hot springs) and are always suggested to stem from a turbulent flow. We report the formation of bacterial streamers in curved microfluidic channels. By using confocal laser microscopy we are able to directly image and characterize the spatial and temporal evolution of these filamentous structures. Such streamers, which always connect the inner corners of opposite sides of the channel, are always located in the middle plane. Numerical simulations of the flow provide evidences for an underlying hydrodynamic mechanism behind the formation of the streamers.

  7. Parametric methods for spatial point processes

    DEFF Research Database (Denmark)

    Møller, Jesper

    is studied in Section 4, and Bayesian inference in Section 5. On one hand, as the development in computer technology and computational statistics continues,computationally-intensive simulation-based methods for likelihood inference probably will play a increasing role for statistical analysis of spatial...... inference procedures for parametric spatial point process models. The widespread use of sensible but ad hoc methods based on summary statistics of the kind studied in Chapter 4.3 have through the last two decades been supplied by likelihood based methods for parametric spatial point process models......(This text is submitted for the volume ‘A Handbook of Spatial Statistics' edited by A.E. Gelfand, P. Diggle, M. Fuentes, and P. Guttorp, to be published by Chapmand and Hall/CRC Press, and planned to appear as Chapter 4.4 with the title ‘Parametric methods'.) 1 Introduction This chapter considers...

  8. On Maximum Entropy and Inference

    Directory of Open Access Journals (Sweden)

    Luigi Gresele

    2017-11-01

    Full Text Available Maximum entropy is a powerful concept that entails a sharp separation between relevant and irrelevant variables. It is typically invoked in inference, once an assumption is made on what the relevant variables are, in order to estimate a model from data, that affords predictions on all other (dependent variables. Conversely, maximum entropy can be invoked to retrieve the relevant variables (sufficient statistics directly from the data, once a model is identified by Bayesian model selection. We explore this approach in the case of spin models with interactions of arbitrary order, and we discuss how relevant interactions can be inferred. In this perspective, the dimensionality of the inference problem is not set by the number of parameters in the model, but by the frequency distribution of the data. We illustrate the method showing its ability to recover the correct model in a few prototype cases and discuss its application on a real dataset.

  9. Mistaking geography for biology: inferring processes from species distributions.

    Science.gov (United States)

    Warren, Dan L; Cardillo, Marcel; Rosauer, Dan F; Bolnick, Daniel I

    2014-10-01

    Over the past few decades, there has been a rapid proliferation of statistical methods that infer evolutionary and ecological processes from data on species distributions. These methods have led to considerable new insights, but they often fail to account for the effects of historical biogeography on present-day species distributions. Because the geography of speciation can lead to patterns of spatial and temporal autocorrelation in the distributions of species within a clade, this can result in misleading inferences about the importance of deterministic processes in generating spatial patterns of biodiversity. In this opinion article, we discuss ways in which patterns of species distributions driven by historical biogeography are often interpreted as evidence of particular evolutionary or ecological processes. We focus on three areas that are especially prone to such misinterpretations: community phylogenetics, environmental niche modelling, and analyses of beta diversity (compositional turnover of biodiversity). Crown Copyright © 2014. Published by Elsevier Ltd. All rights reserved.

  10. Bacterial Diversity across Individual Lichens▿ †

    Science.gov (United States)

    Mushegian, Alexandra A.; Peterson, Celeste N.; Baker, Christopher C. M.; Pringle, Anne

    2011-01-01

    Symbioses are unique habitats for bacteria. We surveyed the spatial diversity of bacterial communities across multiple individuals of closely related lichens using terminal restriction fragment length polymorphism (T-RFLP) and pyrosequencing. Centers of lichens house richer, more consistent assemblages than species-poor and compositionally disparate lichen edges, suggesting that ecological succession plays a role in structuring these communities. PMID:21531831

  11. Cortical hierarchies perform Bayesian causal inference in multisensory perception.

    Directory of Open Access Journals (Sweden)

    Tim Rohe

    2015-02-01

    Full Text Available To form a veridical percept of the environment, the brain needs to integrate sensory signals from a common source but segregate those from independent sources. Thus, perception inherently relies on solving the "causal inference problem." Behaviorally, humans solve this problem optimally as predicted by Bayesian Causal Inference; yet, the underlying neural mechanisms are unexplored. Combining psychophysics, Bayesian modeling, functional magnetic resonance imaging (fMRI, and multivariate decoding in an audiovisual spatial localization task, we demonstrate that Bayesian Causal Inference is performed by a hierarchy of multisensory processes in the human brain. At the bottom of the hierarchy, in auditory and visual areas, location is represented on the basis that the two signals are generated by independent sources (= segregation. At the next stage, in posterior intraparietal sulcus, location is estimated under the assumption that the two signals are from a common source (= forced fusion. Only at the top of the hierarchy, in anterior intraparietal sulcus, the uncertainty about the causal structure of the world is taken into account and sensory signals are combined as predicted by Bayesian Causal Inference. Characterizing the computational operations of signal interactions reveals the hierarchical nature of multisensory perception in human neocortex. It unravels how the brain accomplishes Bayesian Causal Inference, a statistical computation fundamental for perception and cognition. Our results demonstrate how the brain combines information in the face of uncertainty about the underlying causal structure of the world.

  12. Eight challenges in phylodynamic inference

    Directory of Open Access Journals (Sweden)

    Simon D.W. Frost

    2015-03-01

    Full Text Available The field of phylodynamics, which attempts to enhance our understanding of infectious disease dynamics using pathogen phylogenies, has made great strides in the past decade. Basic epidemiological and evolutionary models are now well characterized with inferential frameworks in place. However, significant challenges remain in extending phylodynamic inference to more complex systems. These challenges include accounting for evolutionary complexities such as changing mutation rates, selection, reassortment, and recombination, as well as epidemiological complexities such as stochastic population dynamics, host population structure, and different patterns at the within-host and between-host scales. An additional challenge exists in making efficient inferences from an ever increasing corpus of sequence data.

  13. Problem solving and inference mechanisms

    Energy Technology Data Exchange (ETDEWEB)

    Furukawa, K; Nakajima, R; Yonezawa, A; Goto, S; Aoyama, A

    1982-01-01

    The heart of the fifth generation computer will be powerful mechanisms for problem solving and inference. A deduction-oriented language is to be designed, which will form the core of the whole computing system. The language is based on predicate logic with the extended features of structuring facilities, meta structures and relational data base interfaces. Parallel computation mechanisms and specialized hardware architectures are being investigated to make possible efficient realization of the language features. The project includes research into an intelligent programming system, a knowledge representation language and system, and a meta inference system to be built on the core. 30 references.

  14. Evolution of Bacterial Suicide

    Science.gov (United States)

    Tchernookov, Martin; Nemenman, Ilya

    2013-03-01

    While active, controlled cellular suicide (autolysis) in bacteria is commonly observed, it has been hard to argue that autolysis can be beneficial to an individual who commits it. We propose a theoretical model that predicts that bacterial autolysis is evolutionarily advantageous to an individualand would fixate in physically structured environments for stationary phase colonies. We perform spatially resolved agent-based simulations of the model, which predict that lower mixing in the environment results in fixation of a higher autolysis rate from a single mutated cell, regardless of the colony's genetic diversity. We argue that quorum sensing will fixate as well, even if initially rare, if it is coupled to controlling the autolysis rate. The model does not predict a strong additional competitive advantage for cells where autolysis is controlled by quorum sensing systems that distinguish self from nonself. These predictions are broadly supported by recent experimental results in B. subtilisand S. pneumoniae. Research partially supported by the James S McDonnell Foundation grant No. 220020321 and by HFSP grant No. RGY0084/2011.

  15. Object-Oriented Type Inference

    DEFF Research Database (Denmark)

    Schwartzbach, Michael Ignatieff; Palsberg, Jens

    1991-01-01

    We present a new approach to inferring types in untyped object-oriented programs with inheritance, assignments, and late binding. It guarantees that all messages are understood, annotates the program with type information, allows polymorphic methods, and can be used as the basis of an op...

  16. Inference in hybrid Bayesian networks

    DEFF Research Database (Denmark)

    Lanseth, Helge; Nielsen, Thomas Dyhre; Rumí, Rafael

    2009-01-01

    Since the 1980s, Bayesian Networks (BNs) have become increasingly popular for building statistical models of complex systems. This is particularly true for boolean systems, where BNs often prove to be a more efficient modelling framework than traditional reliability-techniques (like fault trees...... decade's research on inference in hybrid Bayesian networks. The discussions are linked to an example model for estimating human reliability....

  17. Mixed normal inference on multicointegration

    NARCIS (Netherlands)

    Boswijk, H.P.

    2009-01-01

    Asymptotic likelihood analysis of cointegration in I(2) models, see Johansen (1997, 2006), Boswijk (2000) and Paruolo (2000), has shown that inference on most parameters is mixed normal, implying hypothesis test statistics with an asymptotic 2 null distribution. The asymptotic distribution of the

  18. Statistical inference and Aristotle's Rhetoric.

    Science.gov (United States)

    Macdonald, Ranald R

    2004-11-01

    Formal logic operates in a closed system where all the information relevant to any conclusion is present, whereas this is not the case when one reasons about events and states of the world. Pollard and Richardson drew attention to the fact that the reasoning behind statistical tests does not lead to logically justifiable conclusions. In this paper statistical inferences are defended not by logic but by the standards of everyday reasoning. Aristotle invented formal logic, but argued that people mostly get at the truth with the aid of enthymemes--incomplete syllogisms which include arguing from examples, analogies and signs. It is proposed that statistical tests work in the same way--in that they are based on examples, invoke the analogy of a model and use the size of the effect under test as a sign that the chance hypothesis is unlikely. Of existing theories of statistical inference only a weak version of Fisher's takes this into account. Aristotle anticipated Fisher by producing an argument of the form that there were too many cases in which an outcome went in a particular direction for that direction to be plausibly attributed to chance. We can therefore conclude that Aristotle would have approved of statistical inference and there is a good reason for calling this form of statistical inference classical.

  19. Bacterial surface appendages strongly impact nanomechanical and electrokinetic properties of Escherichia coli cells subjected to osmotic stress.

    Directory of Open Access Journals (Sweden)

    Grégory Francius

    Full Text Available The physicochemical properties and dynamics of bacterial envelope, play a major role in bacterial activity. In this study, the morphological, nanomechanical and electrohydrodynamic properties of Escherichia coli K-12 mutant cells were thoroughly investigated as a function of bulk medium ionic strength using atomic force microscopy (AFM and electrokinetics (electrophoresis. Bacteria were differing according to genetic alterations controlling the production of different surface appendages (short and rigid Ag43 adhesins, longer and more flexible type 1 fimbriae and F pilus. From the analysis of the spatially resolved force curves, it is shown that cells elasticity and turgor pressure are not only depending on bulk salt concentration but also on the presence/absence and nature of surface appendage. In 1 mM KNO(3, cells without appendages or cells surrounded by Ag43 exhibit large Young moduli and turgor pressures (∼700-900 kPa and ∼100-300 kPa respectively. Under similar ionic strength condition, a dramatic ∼50% to ∼70% decrease of these nanomechanical parameters was evidenced for cells with appendages. Qualitatively, such dependence of nanomechanical behavior on surface organization remains when increasing medium salt content to 100 mM, even though, quantitatively, differences are marked to a much smaller extent. Additionally, for a given surface appendage, the magnitude of the nanomechanical parameters decreases significantly when increasing bulk salt concentration. This effect is ascribed to a bacterial exoosmotic water loss resulting in a combined contraction of bacterial cytoplasm together with an electrostatically-driven shrinkage of the surface appendages. The former process is demonstrated upon AFM analysis, while the latter, inaccessible upon AFM imaging, is inferred from electrophoretic data interpreted according to advanced soft particle electrokinetic theory. Altogether, AFM and electrokinetic results clearly demonstrate the

  20. Dispersal networks for enhancing bacterial degradation in heterogeneous environments

    International Nuclear Information System (INIS)

    Banitz, Thomas; Wick, Lukas Y.; Fetzer, Ingo; Frank, Karin; Harms, Hauke; Johst, Karin

    2011-01-01

    Successful biodegradation of organic soil pollutants depends on their bioavailability to catabolically active microorganisms. In particular, environmental heterogeneities often limit bacterial access to pollutants. Experimental and modelling studies revealed that fungal networks can facilitate bacterial dispersal and may thereby improve pollutant bioavailability. Here, we investigate the influence of such bacterial dispersal networks on biodegradation performance under spatially heterogeneous abiotic conditions using a process-based simulation model. To match typical situations in polluted soils, two types of abiotic conditions are studied: heterogeneous bacterial dispersal conditions and heterogeneous initial resource distributions. The model predicts that networks facilitating bacterial dispersal can enhance biodegradation performance for a wide range of these conditions. Additionally, the time horizon over which this performance is assessed and the network's spatial configuration are key factors determining the degree of biodegradation improvement. Our results support the idea of stimulating the establishment of fungal mycelia for enhanced bioremediation of polluted soils. - Highlights: → Bacterial dispersal networks can considerably improve biodegradation performance. → They facilitate bacterial access to dispersal-limited areas and remote resources. → Abiotic conditions, time horizon and network structure govern the improvements. → Stimulating the establishment of fungal mycelia promises enhanced soil remediation. - Simulation modelling demonstrates that fungus-mediated bacterial dispersal can considerably improve the bioavailability of organic pollutants under spatially heterogeneous abiotic conditions typical for water-unsaturated soils.

  1. Dispersal networks for enhancing bacterial degradation in heterogeneous environments

    Energy Technology Data Exchange (ETDEWEB)

    Banitz, Thomas, E-mail: thomas.banitz@ufz.de [Department of Ecological Modelling, UFZ - Helmholtz Centre for Environmental Research, Permoserstr. 15, 04318 Leipzig (Germany); Wick, Lukas Y.; Fetzer, Ingo [Department of Environmental Microbiology, UFZ - Helmholtz Centre for Environmental Research, Permoserstr. 15, 04318 Leipzig (Germany); Frank, Karin [Department of Ecological Modelling, UFZ - Helmholtz Centre for Environmental Research, Permoserstr. 15, 04318 Leipzig (Germany); Harms, Hauke [Department of Environmental Microbiology, UFZ - Helmholtz Centre for Environmental Research, Permoserstr. 15, 04318 Leipzig (Germany); Johst, Karin [Department of Ecological Modelling, UFZ - Helmholtz Centre for Environmental Research, Permoserstr. 15, 04318 Leipzig (Germany)

    2011-10-15

    Successful biodegradation of organic soil pollutants depends on their bioavailability to catabolically active microorganisms. In particular, environmental heterogeneities often limit bacterial access to pollutants. Experimental and modelling studies revealed that fungal networks can facilitate bacterial dispersal and may thereby improve pollutant bioavailability. Here, we investigate the influence of such bacterial dispersal networks on biodegradation performance under spatially heterogeneous abiotic conditions using a process-based simulation model. To match typical situations in polluted soils, two types of abiotic conditions are studied: heterogeneous bacterial dispersal conditions and heterogeneous initial resource distributions. The model predicts that networks facilitating bacterial dispersal can enhance biodegradation performance for a wide range of these conditions. Additionally, the time horizon over which this performance is assessed and the network's spatial configuration are key factors determining the degree of biodegradation improvement. Our results support the idea of stimulating the establishment of fungal mycelia for enhanced bioremediation of polluted soils. - Highlights: > Bacterial dispersal networks can considerably improve biodegradation performance. > They facilitate bacterial access to dispersal-limited areas and remote resources. > Abiotic conditions, time horizon and network structure govern the improvements. > Stimulating the establishment of fungal mycelia promises enhanced soil remediation. - Simulation modelling demonstrates that fungus-mediated bacterial dispersal can considerably improve the bioavailability of organic pollutants under spatially heterogeneous abiotic conditions typical for water-unsaturated soils.

  2. Statistical learning and selective inference.

    Science.gov (United States)

    Taylor, Jonathan; Tibshirani, Robert J

    2015-06-23

    We describe the problem of "selective inference." This addresses the following challenge: Having mined a set of data to find potential associations, how do we properly assess the strength of these associations? The fact that we have "cherry-picked"--searched for the strongest associations--means that we must set a higher bar for declaring significant the associations that we see. This challenge becomes more important in the era of big data and complex statistical modeling. The cherry tree (dataset) can be very large and the tools for cherry picking (statistical learning methods) are now very sophisticated. We describe some recent new developments in selective inference and illustrate their use in forward stepwise regression, the lasso, and principal components analysis.

  3. Bayesian inference with ecological applications

    CERN Document Server

    Link, William A

    2009-01-01

    This text is written to provide a mathematically sound but accessible and engaging introduction to Bayesian inference specifically for environmental scientists, ecologists and wildlife biologists. It emphasizes the power and usefulness of Bayesian methods in an ecological context. The advent of fast personal computers and easily available software has simplified the use of Bayesian and hierarchical models . One obstacle remains for ecologists and wildlife biologists, namely the near absence of Bayesian texts written specifically for them. The book includes many relevant examples, is supported by software and examples on a companion website and will become an essential grounding in this approach for students and research ecologists. Engagingly written text specifically designed to demystify a complex subject Examples drawn from ecology and wildlife research An essential grounding for graduate and research ecologists in the increasingly prevalent Bayesian approach to inference Companion website with analyt...

  4. Statistical inference an integrated approach

    CERN Document Server

    Migon, Helio S; Louzada, Francisco

    2014-01-01

    Introduction Information The concept of probability Assessing subjective probabilities An example Linear algebra and probability Notation Outline of the bookElements of Inference Common statistical modelsLikelihood-based functions Bayes theorem Exchangeability Sufficiency and exponential family Parameter elimination Prior Distribution Entirely subjective specification Specification through functional forms Conjugacy with the exponential family Non-informative priors Hierarchical priors Estimation Introduction to decision theoryBayesian point estimation Classical point estimation Empirical Bayes estimation Comparison of estimators Interval estimation Estimation in the Normal model Approximating Methods The general problem of inference Optimization techniquesAsymptotic theory Other analytical approximations Numerical integration methods Simulation methods Hypothesis Testing Introduction Classical hypothesis testingBayesian hypothesis testing Hypothesis testing and confidence intervalsAsymptotic tests Prediction...

  5. Bayesian inference on proportional elections.

    Directory of Open Access Journals (Sweden)

    Gabriel Hideki Vatanabe Brunello

    Full Text Available Polls for majoritarian voting systems usually show estimates of the percentage of votes for each candidate. However, proportional vote systems do not necessarily guarantee the candidate with the most percentage of votes will be elected. Thus, traditional methods used in majoritarian elections cannot be applied on proportional elections. In this context, the purpose of this paper was to perform a Bayesian inference on proportional elections considering the Brazilian system of seats distribution. More specifically, a methodology to answer the probability that a given party will have representation on the chamber of deputies was developed. Inferences were made on a Bayesian scenario using the Monte Carlo simulation technique, and the developed methodology was applied on data from the Brazilian elections for Members of the Legislative Assembly and Federal Chamber of Deputies in 2010. A performance rate was also presented to evaluate the efficiency of the methodology. Calculations and simulations were carried out using the free R statistical software.

  6. Causal inference based on counterfactuals

    Directory of Open Access Journals (Sweden)

    Höfler M

    2005-09-01

    Full Text Available Abstract Background The counterfactual or potential outcome model has become increasingly standard for causal inference in epidemiological and medical studies. Discussion This paper provides an overview on the counterfactual and related approaches. A variety of conceptual as well as practical issues when estimating causal effects are reviewed. These include causal interactions, imperfect experiments, adjustment for confounding, time-varying exposures, competing risks and the probability of causation. It is argued that the counterfactual model of causal effects captures the main aspects of causality in health sciences and relates to many statistical procedures. Summary Counterfactuals are the basis of causal inference in medicine and epidemiology. Nevertheless, the estimation of counterfactual differences pose several difficulties, primarily in observational studies. These problems, however, reflect fundamental barriers only when learning from observations, and this does not invalidate the counterfactual concept.

  7. System Support for Forensic Inference

    Science.gov (United States)

    Gehani, Ashish; Kirchner, Florent; Shankar, Natarajan

    Digital evidence is playing an increasingly important role in prosecuting crimes. The reasons are manifold: financially lucrative targets are now connected online, systems are so complex that vulnerabilities abound and strong digital identities are being adopted, making audit trails more useful. If the discoveries of forensic analysts are to hold up to scrutiny in court, they must meet the standard for scientific evidence. Software systems are currently developed without consideration of this fact. This paper argues for the development of a formal framework for constructing “digital artifacts” that can serve as proxies for physical evidence; a system so imbued would facilitate sound digital forensic inference. A case study involving a filesystem augmentation that provides transparent support for forensic inference is described.

  8. Probability biases as Bayesian inference

    Directory of Open Access Journals (Sweden)

    Andre; C. R. Martins

    2006-11-01

    Full Text Available In this article, I will show how several observed biases in human probabilistic reasoning can be partially explained as good heuristics for making inferences in an environment where probabilities have uncertainties associated to them. Previous results show that the weight functions and the observed violations of coalescing and stochastic dominance can be understood from a Bayesian point of view. We will review those results and see that Bayesian methods should also be used as part of the explanation behind other known biases. That means that, although the observed errors are still errors under the be understood as adaptations to the solution of real life problems. Heuristics that allow fast evaluations and mimic a Bayesian inference would be an evolutionary advantage, since they would give us an efficient way of making decisions. %XX In that sense, it should be no surprise that humans reason with % probability as it has been observed.

  9. Statistical inference on residual life

    CERN Document Server

    Jeong, Jong-Hyeon

    2014-01-01

    This is a monograph on the concept of residual life, which is an alternative summary measure of time-to-event data, or survival data. The mean residual life has been used for many years under the name of life expectancy, so it is a natural concept for summarizing survival or reliability data. It is also more interpretable than the popular hazard function, especially for communications between patients and physicians regarding the efficacy of a new drug in the medical field. This book reviews existing statistical methods to infer the residual life distribution. The review and comparison includes existing inference methods for mean and median, or quantile, residual life analysis through medical data examples. The concept of the residual life is also extended to competing risks analysis. The targeted audience includes biostatisticians, graduate students, and PhD (bio)statisticians. Knowledge in survival analysis at an introductory graduate level is advisable prior to reading this book.

  10. Nonparametric Bayesian inference in biostatistics

    CERN Document Server

    Müller, Peter

    2015-01-01

    As chapters in this book demonstrate, BNP has important uses in clinical sciences and inference for issues like unknown partitions in genomics. Nonparametric Bayesian approaches (BNP) play an ever expanding role in biostatistical inference from use in proteomics to clinical trials. Many research problems involve an abundance of data and require flexible and complex probability models beyond the traditional parametric approaches. As this book's expert contributors show, BNP approaches can be the answer. Survival Analysis, in particular survival regression, has traditionally used BNP, but BNP's potential is now very broad. This applies to important tasks like arrangement of patients into clinically meaningful subpopulations and segmenting the genome into functionally distinct regions. This book is designed to both review and introduce application areas for BNP. While existing books provide theoretical foundations, this book connects theory to practice through engaging examples and research questions. Chapters c...

  11. Statistical inference a short course

    CERN Document Server

    Panik, Michael J

    2012-01-01

    A concise, easily accessible introduction to descriptive and inferential techniques Statistical Inference: A Short Course offers a concise presentation of the essentials of basic statistics for readers seeking to acquire a working knowledge of statistical concepts, measures, and procedures. The author conducts tests on the assumption of randomness and normality, provides nonparametric methods when parametric approaches might not work. The book also explores how to determine a confidence interval for a population median while also providing coverage of ratio estimation, randomness, and causal

  12. On Quantum Statistical Inference, II

    OpenAIRE

    Barndorff-Nielsen, O. E.; Gill, R. D.; Jupp, P. E.

    2003-01-01

    Interest in problems of statistical inference connected to measurements of quantum systems has recently increased substantially, in step with dramatic new developments in experimental techniques for studying small quantum systems. Furthermore, theoretical developments in the theory of quantum measurements have brought the basic mathematical framework for the probability calculations much closer to that of classical probability theory. The present paper reviews this field and proposes and inte...

  13. Nonparametric predictive inference in reliability

    International Nuclear Information System (INIS)

    Coolen, F.P.A.; Coolen-Schrijner, P.; Yan, K.J.

    2002-01-01

    We introduce a recently developed statistical approach, called nonparametric predictive inference (NPI), to reliability. Bounds for the survival function for a future observation are presented. We illustrate how NPI can deal with right-censored data, and discuss aspects of competing risks. We present possible applications of NPI for Bernoulli data, and we briefly outline applications of NPI for replacement decisions. The emphasis is on introduction and illustration of NPI in reliability contexts, detailed mathematical justifications are presented elsewhere

  14. Variational inference & deep learning : A new synthesis

    NARCIS (Netherlands)

    Kingma, D.P.

    2017-01-01

    In this thesis, Variational Inference and Deep Learning: A New Synthesis, we propose novel solutions to the problems of variational (Bayesian) inference, generative modeling, representation learning, semi-supervised learning, and stochastic optimization.

  15. Variational inference & deep learning: A new synthesis

    OpenAIRE

    Kingma, D.P.

    2017-01-01

    In this thesis, Variational Inference and Deep Learning: A New Synthesis, we propose novel solutions to the problems of variational (Bayesian) inference, generative modeling, representation learning, semi-supervised learning, and stochastic optimization.

  16. Continuous Integrated Invariant Inference, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — The proposed project will develop a new technique for invariant inference and embed this and other current invariant inference and checking techniques in an...

  17. Variations on Bayesian Prediction and Inference

    Science.gov (United States)

    2016-05-09

    inference 2.2.1 Background There are a number of statistical inference problems that are not generally formulated via a full probability model...problem of inference about an unknown parameter, the Bayesian approach requires a full probability 1. REPORT DATE (DD-MM-YYYY) 4. TITLE AND...the problem of inference about an unknown parameter, the Bayesian approach requires a full probability model/likelihood which can be an obstacle

  18. Adaptive Inference on General Graphical Models

    OpenAIRE

    Acar, Umut A.; Ihler, Alexander T.; Mettu, Ramgopal; Sumer, Ozgur

    2012-01-01

    Many algorithms and applications involve repeatedly solving variations of the same inference problem; for example we may want to introduce new evidence to the model or perform updates to conditional dependencies. The goal of adaptive inference is to take advantage of what is preserved in the model and perform inference more rapidly than from scratch. In this paper, we describe techniques for adaptive inference on general graphs that support marginal computation and updates to the conditional ...

  19. Bacterial lung abscess

    International Nuclear Information System (INIS)

    Groskin, S.A.; Panicek, D.M.; Ewing, D.K.; Rivera, F.; Math, K.; Teixeira, J.; Heitzman, E.R.

    1987-01-01

    A retrospective review of patients with bacterial lung abscess was carried out. Demographic, clinical, and radiographical features of this patient group are compared with similar data from patients with empyema and/or cavitated lung carcinoma; differential diagnostic points are stressed. The entity of radiographically occult lung abscess is discussed. Complications associated with bacterial lung abscess are discussed. Current therapeutic options and treatment philosophy for patients with bacterial lung abscess are noted

  20. Peritonitis - spontaneous bacterial

    Science.gov (United States)

    Spontaneous bacterial peritonitis (SBP); Ascites - peritonitis; Cirrhosis - peritonitis ... who are on peritoneal dialysis for kidney failure. Peritonitis may have other causes . These include infection from ...

  1. More than one kind of inference: re-examining what's learned in feature inference and classification.

    Science.gov (United States)

    Sweller, Naomi; Hayes, Brett K

    2010-08-01

    Three studies examined how task demands that impact on attention to typical or atypical category features shape the category representations formed through classification learning and inference learning. During training categories were learned via exemplar classification or by inferring missing exemplar features. In the latter condition inferences were made about missing typical features alone (typical feature inference) or about both missing typical and atypical features (mixed feature inference). Classification and mixed feature inference led to the incorporation of typical and atypical features into category representations, with both kinds of features influencing inferences about familiar (Experiments 1 and 2) and novel (Experiment 3) test items. Those in the typical inference condition focused primarily on typical features. Together with formal modelling, these results challenge previous accounts that have characterized inference learning as producing a focus on typical category features. The results show that two different kinds of inference learning are possible and that these are subserved by different kinds of category representations.

  2. Generative inference for cultural evolution.

    Science.gov (United States)

    Kandler, Anne; Powell, Adam

    2018-04-05

    One of the major challenges in cultural evolution is to understand why and how various forms of social learning are used in human populations, both now and in the past. To date, much of the theoretical work on social learning has been done in isolation of data, and consequently many insights focus on revealing the learning processes or the distributions of cultural variants that are expected to have evolved in human populations. In population genetics, recent methodological advances have allowed a greater understanding of the explicit demographic and/or selection mechanisms that underlie observed allele frequency distributions across the globe, and their change through time. In particular, generative frameworks-often using coalescent-based simulation coupled with approximate Bayesian computation (ABC)-have provided robust inferences on the human past, with no reliance on a priori assumptions of equilibrium. Here, we demonstrate the applicability and utility of generative inference approaches to the field of cultural evolution. The framework advocated here uses observed population-level frequency data directly to establish the likely presence or absence of particular hypothesized learning strategies. In this context, we discuss the problem of equifinality and argue that, in the light of sparse cultural data and the multiplicity of possible social learning processes, the exclusion of those processes inconsistent with the observed data might be the most instructive outcome. Finally, we summarize the findings of generative inference approaches applied to a number of case studies.This article is part of the theme issue 'Bridging cultural gaps: interdisciplinary studies in human cultural evolution'. © 2018 The Author(s).

  3. Minerals in soil select distinct bacterial communities in their microhabitats.

    Science.gov (United States)

    Carson, Jennifer K; Campbell, Louise; Rooney, Deirdre; Clipson, Nicholas; Gleeson, Deirdre B

    2009-03-01

    We tested the hypothesis that different minerals in soil select distinct bacterial communities in their microhabitats. Mica (M), basalt (B) and rock phosphate (RP) were incubated separately in soil planted with Trifolium subterraneum, Lolium rigidum or left unplanted. After 70 days, the mineral and soil fractions were separated by sieving. Automated ribosomal intergenic spacer analysis was used to determine whether the bacterial community structure was affected by the mineral, fraction and plant treatments. Principal coordinate plots showed clustering of bacterial communities from different fraction and mineral treatments, but not from different plant treatments. Permutational multivariate anova (permanova) showed that the microhabitats of M, B and RP selected bacterial communities different from each other in unplanted and L. rigidum, and in T. subterraneum, bacterial communities from M and B differed (Ppermanova also showed that each mineral fraction selected bacterial communities different from the surrounding soil fraction (P<0.05). This study shows that the structure of bacterial communities in soil is influenced by the mineral substrates in their microhabitat and that minerals in soil play a greater role in bacterial ecology than simply providing an inert matrix for bacterial growth. This study suggests that mineral heterogeneity in soil contributes to the spatial variation in bacterial communities.

  4. Evolution in Mind: Evolutionary Dynamics, Cognitive Processes, and Bayesian Inference.

    Science.gov (United States)

    Suchow, Jordan W; Bourgin, David D; Griffiths, Thomas L

    2017-07-01

    Evolutionary theory describes the dynamics of population change in settings affected by reproduction, selection, mutation, and drift. In the context of human cognition, evolutionary theory is most often invoked to explain the origins of capacities such as language, metacognition, and spatial reasoning, framing them as functional adaptations to an ancestral environment. However, evolutionary theory is useful for understanding the mind in a second way: as a mathematical framework for describing evolving populations of thoughts, ideas, and memories within a single mind. In fact, deep correspondences exist between the mathematics of evolution and of learning, with perhaps the deepest being an equivalence between certain evolutionary dynamics and Bayesian inference. This equivalence permits reinterpretation of evolutionary processes as algorithms for Bayesian inference and has relevance for understanding diverse cognitive capacities, including memory and creativity. Copyright © 2017 Elsevier Ltd. All rights reserved.

  5. EEG Based Inference of Spatio-Temporal Brain Dynamics

    DEFF Research Database (Denmark)

    Hansen, Sofie Therese

    Electroencephalography (EEG) provides a measure of brain activity and has improved our understanding of the brain immensely. However, there is still much to be learned and the full potential of EEG is yet to be realized. In this thesis we suggest to improve the information gain of EEG using three...... different approaches; 1) by recovery of the EEG sources, 2) by representing and inferring the propagation path of EEG sources, and 3) by combining EEG with functional magnetic resonance imaging (fMRI). The common goal of the methods, and thus of this thesis, is to improve the spatial dimension of EEG...... recovery ability. The forward problem describes the propagation of neuronal activity in the brain to the EEG electrodes on the scalp. The geometry and conductivity of the head layers are normally required to model this path. We propose a framework for inferring forward models which is based on the EEG...

  6. sick: The Spectroscopic Inference Crank

    Science.gov (United States)

    Casey, Andrew R.

    2016-03-01

    There exists an inordinate amount of spectral data in both public and private astronomical archives that remain severely under-utilized. The lack of reliable open-source tools for analyzing large volumes of spectra contributes to this situation, which is poised to worsen as large surveys successively release orders of magnitude more spectra. In this article I introduce sick, the spectroscopic inference crank, a flexible and fast Bayesian tool for inferring astrophysical parameters from spectra. sick is agnostic to the wavelength coverage, resolving power, or general data format, allowing any user to easily construct a generative model for their data, regardless of its source. sick can be used to provide a nearest-neighbor estimate of model parameters, a numerically optimized point estimate, or full Markov Chain Monte Carlo sampling of the posterior probability distributions. This generality empowers any astronomer to capitalize on the plethora of published synthetic and observed spectra, and make precise inferences for a host of astrophysical (and nuisance) quantities. Model intensities can be reliably approximated from existing grids of synthetic or observed spectra using linear multi-dimensional interpolation, or a Cannon-based model. Additional phenomena that transform the data (e.g., redshift, rotational broadening, continuum, spectral resolution) are incorporated as free parameters and can be marginalized away. Outlier pixels (e.g., cosmic rays or poorly modeled regimes) can be treated with a Gaussian mixture model, and a noise model is included to account for systematically underestimated variance. Combining these phenomena into a scalar-justified, quantitative model permits precise inferences with credible uncertainties on noisy data. I describe the common model features, the implementation details, and the default behavior, which is balanced to be suitable for most astronomical applications. Using a forward model on low-resolution, high signal

  7. Inferring network structure from cascades

    Science.gov (United States)

    Ghonge, Sushrut; Vural, Dervis Can

    2017-07-01

    Many physical, biological, and social phenomena can be described by cascades taking place on a network. Often, the activity can be empirically observed, but not the underlying network of interactions. In this paper we offer three topological methods to infer the structure of any directed network given a set of cascade arrival times. Our formulas hold for a very general class of models where the activation probability of a node is a generic function of its degree and the number of its active neighbors. We report high success rates for synthetic and real networks, for several different cascade models.

  8. SICK: THE SPECTROSCOPIC INFERENCE CRANK

    Energy Technology Data Exchange (ETDEWEB)

    Casey, Andrew R., E-mail: arc@ast.cam.ac.uk [Institute of Astronomy, University of Cambridge, Madingley Road, Cambdridge, CB3 0HA (United Kingdom)

    2016-03-15

    There exists an inordinate amount of spectral data in both public and private astronomical archives that remain severely under-utilized. The lack of reliable open-source tools for analyzing large volumes of spectra contributes to this situation, which is poised to worsen as large surveys successively release orders of magnitude more spectra. In this article I introduce sick, the spectroscopic inference crank, a flexible and fast Bayesian tool for inferring astrophysical parameters from spectra. sick is agnostic to the wavelength coverage, resolving power, or general data format, allowing any user to easily construct a generative model for their data, regardless of its source. sick can be used to provide a nearest-neighbor estimate of model parameters, a numerically optimized point estimate, or full Markov Chain Monte Carlo sampling of the posterior probability distributions. This generality empowers any astronomer to capitalize on the plethora of published synthetic and observed spectra, and make precise inferences for a host of astrophysical (and nuisance) quantities. Model intensities can be reliably approximated from existing grids of synthetic or observed spectra using linear multi-dimensional interpolation, or a Cannon-based model. Additional phenomena that transform the data (e.g., redshift, rotational broadening, continuum, spectral resolution) are incorporated as free parameters and can be marginalized away. Outlier pixels (e.g., cosmic rays or poorly modeled regimes) can be treated with a Gaussian mixture model, and a noise model is included to account for systematically underestimated variance. Combining these phenomena into a scalar-justified, quantitative model permits precise inferences with credible uncertainties on noisy data. I describe the common model features, the implementation details, and the default behavior, which is balanced to be suitable for most astronomical applications. Using a forward model on low-resolution, high signal

  9. Bayesian inference for Hawkes processes

    DEFF Research Database (Denmark)

    Rasmussen, Jakob Gulddahl

    The Hawkes process is a practically and theoretically important class of point processes, but parameter-estimation for such a process can pose various problems. In this paper we explore and compare two approaches to Bayesian inference. The first approach is based on the so-called conditional...... intensity function, while the second approach is based on an underlying clustering and branching structure in the Hawkes process. For practical use, MCMC (Markov chain Monte Carlo) methods are employed. The two approaches are compared numerically using three examples of the Hawkes process....

  10. Bayesian inference for Hawkes processes

    DEFF Research Database (Denmark)

    Rasmussen, Jakob Gulddahl

    2013-01-01

    The Hawkes process is a practically and theoretically important class of point processes, but parameter-estimation for such a process can pose various problems. In this paper we explore and compare two approaches to Bayesian inference. The first approach is based on the so-called conditional...... intensity function, while the second approach is based on an underlying clustering and branching structure in the Hawkes process. For practical use, MCMC (Markov chain Monte Carlo) methods are employed. The two approaches are compared numerically using three examples of the Hawkes process....

  11. Inference in hybrid Bayesian networks

    International Nuclear Information System (INIS)

    Langseth, Helge; Nielsen, Thomas D.; Rumi, Rafael; Salmeron, Antonio

    2009-01-01

    Since the 1980s, Bayesian networks (BNs) have become increasingly popular for building statistical models of complex systems. This is particularly true for boolean systems, where BNs often prove to be a more efficient modelling framework than traditional reliability techniques (like fault trees and reliability block diagrams). However, limitations in the BNs' calculation engine have prevented BNs from becoming equally popular for domains containing mixtures of both discrete and continuous variables (the so-called hybrid domains). In this paper we focus on these difficulties, and summarize some of the last decade's research on inference in hybrid Bayesian networks. The discussions are linked to an example model for estimating human reliability.

  12. SICK: THE SPECTROSCOPIC INFERENCE CRANK

    International Nuclear Information System (INIS)

    Casey, Andrew R.

    2016-01-01

    There exists an inordinate amount of spectral data in both public and private astronomical archives that remain severely under-utilized. The lack of reliable open-source tools for analyzing large volumes of spectra contributes to this situation, which is poised to worsen as large surveys successively release orders of magnitude more spectra. In this article I introduce sick, the spectroscopic inference crank, a flexible and fast Bayesian tool for inferring astrophysical parameters from spectra. sick is agnostic to the wavelength coverage, resolving power, or general data format, allowing any user to easily construct a generative model for their data, regardless of its source. sick can be used to provide a nearest-neighbor estimate of model parameters, a numerically optimized point estimate, or full Markov Chain Monte Carlo sampling of the posterior probability distributions. This generality empowers any astronomer to capitalize on the plethora of published synthetic and observed spectra, and make precise inferences for a host of astrophysical (and nuisance) quantities. Model intensities can be reliably approximated from existing grids of synthetic or observed spectra using linear multi-dimensional interpolation, or a Cannon-based model. Additional phenomena that transform the data (e.g., redshift, rotational broadening, continuum, spectral resolution) are incorporated as free parameters and can be marginalized away. Outlier pixels (e.g., cosmic rays or poorly modeled regimes) can be treated with a Gaussian mixture model, and a noise model is included to account for systematically underestimated variance. Combining these phenomena into a scalar-justified, quantitative model permits precise inferences with credible uncertainties on noisy data. I describe the common model features, the implementation details, and the default behavior, which is balanced to be suitable for most astronomical applications. Using a forward model on low-resolution, high signal

  13. Prevention of bacterial adhesion

    DEFF Research Database (Denmark)

    Klemm, Per; Vejborg, Rebecca Munk; Hancock, Viktoria

    2010-01-01

    . As such, adhesion represents the Achilles heel of crucial pathogenic functions. It follows that interference with adhesion can reduce bacterial virulence. Here, we illustrate this important topic with examples of techniques being developed that can inhibit bacterial adhesion. Some of these will become...

  14. Bacterial Ventures into Multicellularity: Collectivism through Individuality.

    Directory of Open Access Journals (Sweden)

    Simon van Vliet

    2015-06-01

    Full Text Available Multicellular eukaryotes can perform functions that exceed the possibilities of an individual cell. These functions emerge through interactions between differentiated cells that are precisely arranged in space. Bacteria also form multicellular collectives that consist of differentiated but genetically identical cells. How does the functionality of these collectives depend on the spatial arrangement of the differentiated bacteria? In a previous issue of PLOS Biology, van Gestel and colleagues reported an elegant example of how the spatial arrangement of differentiated cells gives rise to collective behavior in Bacillus subtilus colonies, further demonstrating the similarity of bacterial collectives to higher multicellular organisms.

  15. Anaerobic bacterial quantitation of Yucca Mountain, Nevada DOE site samples

    International Nuclear Information System (INIS)

    Clarkson, W.W.; Krumholz, L.R.; Suflita, J.M.

    1996-01-01

    Anaerobic bacteria were studied from samples of excavated rock material as one phase of the overall Yucca Mountain site characterization effort. An indication of the abundance of important groups of anaerobic bacteria would enable inferences to be made regarding the natural history of the site and allow for more complete risk evaluation of the site as a nuclear repository. Six bacterial groups were investigated including anaerobic heterotrophs, acetogens, methanogens, sulfate-, nitrate-, and iron-reducing bacteria. The purpose of this portion of the study was to detect and quantify the aforementioned bacterial groups

  16. Spatial Operations

    Directory of Open Access Journals (Sweden)

    Anda VELICANU

    2010-09-01

    Full Text Available This paper contains a brief description of the most important operations that can be performed on spatial data such as spatial queries, create, update, insert, delete operations, conversions, operations on the map or analysis on grid cells. Each operation has a graphical example and some of them have code examples in Oracle and PostgreSQL.

  17. Spatializing Time

    DEFF Research Database (Denmark)

    Thomsen, Bodil Marie Stavning

    2011-01-01

    The article analyses some of artist Søren Lose's photographic installations in which time, history and narration is reflected in the creation of allegoric, spatial relations.......The article analyses some of artist Søren Lose's photographic installations in which time, history and narration is reflected in the creation of allegoric, spatial relations....

  18. Spatial Computation

    Science.gov (United States)

    2003-12-01

    Computation and today’s microprocessors with the approach to operating system architecture, and the controversy between microkernels and monolithic kernels...Both Spatial Computation and microkernels break away a relatively monolithic architecture into in- dividual lightweight pieces, well specialized...for their particular functionality. Spatial Computation removes global signals and control, in the same way microkernels remove the global address

  19. The relationship between sea ice bacterial community structure and biogeochemistry: A synthesis of current knowledge and known unknowns

    Directory of Open Access Journals (Sweden)

    Jeff S. Bowman

    2015-10-01

    Full Text Available Abstract Sea ice plays an important role in high latitude biogeochemical cycles, ecosystems, and climate. A complete understanding of how sea ice biogeochemistry contributes to these processes must take into account the metabolic functions of the sea ice bacterial community. While the roles of sea ice bacteria in the carbon cycle and sea ice microbial loop are evidenced by high rates of bacterial production (BP, their metabolic diversity extends far beyond heterotrophy, and their functionality encompasses much more than carbon turnover. Work over the last three decades has identified an active role for sea ice bacteria in phosphate and nitrogen cycling, mutualistic partnerships with ice algae, and even prokaryotic carbon fixation. To better understand the role of sea ice bacteria in the carbon cycle the existing sea ice BP and primary production data were synthesized. BP in sea ice was poorly correlated with primary production, but had a strong, variable relationship with chlorophyll a, with a positive correlation below 50 mg chlorophyll a m-3 and a negative correlation above this value. These results concur with previous work suggesting that BP can be inhibited by grazing or the production of bacteriostatic compounds. To extend existing observations and predictions of other community functions a metabolic inference technique was used on the available 16S rRNA gene data. This analysis provided taxonomic support for some observed metabolic processes, as well as underexplored processes such as sulfur oxidation and nitrogen fixation. The decreasing spatial and temporal extent of sea ice, and altered timing of ice formation and melt, are likely to impact the structure and function of sea ice bacterial communities. An adequate modeling framework and studies that can resolve the functional dynamics of the sea ice bacterial community, such as community gene expression studies, are urgently needed to predict future change.

  20. Statistical inference for Cox processes

    DEFF Research Database (Denmark)

    Møller, Jesper; Waagepetersen, Rasmus Plenge

    2002-01-01

    Research has generated a number of advances in methods for spatial cluster modelling in recent years, particularly in the area of Bayesian cluster modelling. Along with these advances has come an explosion of interest in the potential applications of this work, especially in epidemiology and genome...... research.   In one integrated volume, this book reviews the state-of-the-art in spatial clustering and spatial cluster modelling, bringing together research and applications previously scattered throughout the literature. It begins with an overview of the field, then presents a series of chapters...... that illuminate the nature and purpose of cluster modelling within different application areas, including astrophysics, epidemiology, ecology, and imaging. The focus then shifts to methods, with discussions on point and object process modelling, perfect sampling of cluster processes, partitioning in space...

  1. Autoproteolytic Activation of Bacterial Toxins

    Directory of Open Access Journals (Sweden)

    Aimee Shen

    2010-05-01

    Full Text Available Protease domains within toxins typically act as the primary effector domain within target cells. By contrast, the primary function of the cysteine protease domain (CPD in Multifunctional Autoprocessing RTX-like (MARTX and Clostridium sp. glucosylating toxin families is to proteolytically cleave the toxin and release its cognate effector domains. The CPD becomes activated upon binding to the eukaryotic-specific small molecule, inositol hexakisphosphate (InsP6, which is found abundantly in the eukaryotic cytosol. This property allows the CPD to spatially and temporally regulate toxin activation, making it a prime candidate for developing anti-toxin therapeutics. In this review, we summarize recent findings related to defining the regulation of toxin function by the CPD and the development of inhibitors to prevent CPD-mediated activation of bacterial toxins.

  2. Postviral Complications: Bacterial Pneumonia.

    Science.gov (United States)

    Prasso, Jason E; Deng, Jane C

    2017-03-01

    Secondary bacterial pneumonia after viral respiratory infection remains a significant source of morbidity and mortality. Susceptibility is mediated by a variety of viral and bacterial factors, and complex interactions with the host immune system. Prevention and treatment strategies are limited to influenza vaccination and antibiotics/antivirals respectively. Novel approaches to identifying the individuals with influenza who are at increased risk for secondary bacterial pneumonias are urgently needed. Given the threat of further pandemics and the heightened prevalence of these viruses, more research into the immunologic mechanisms of this disease is warranted with the hope of discovering new potential therapies. Published by Elsevier Inc.

  3. Lower complexity bounds for lifted inference

    DEFF Research Database (Denmark)

    Jaeger, Manfred

    2015-01-01

    instances of the model. Numerous approaches for such “lifted inference” techniques have been proposed. While it has been demonstrated that these techniques will lead to significantly more efficient inference on some specific models, there are only very recent and still quite restricted results that show...... the feasibility of lifted inference on certain syntactically defined classes of models. Lower complexity bounds that imply some limitations for the feasibility of lifted inference on more expressive model classes were established earlier in Jaeger (2000; Jaeger, M. 2000. On the complexity of inference about...... that under the assumption that NETIME≠ETIME, there is no polynomial lifted inference algorithm for knowledge bases of weighted, quantifier-, and function-free formulas. Further strengthening earlier results, this is also shown to hold for approximate inference and for knowledge bases not containing...

  4. Statistical inference for financial engineering

    CERN Document Server

    Taniguchi, Masanobu; Ogata, Hiroaki; Taniai, Hiroyuki

    2014-01-01

    This monograph provides the fundamentals of statistical inference for financial engineering and covers some selected methods suitable for analyzing financial time series data. In order to describe the actual financial data, various stochastic processes, e.g. non-Gaussian linear processes, non-linear processes, long-memory processes, locally stationary processes etc. are introduced and their optimal estimation is considered as well. This book also includes several statistical approaches, e.g., discriminant analysis, the empirical likelihood method, control variate method, quantile regression, realized volatility etc., which have been recently developed and are considered to be powerful tools for analyzing the financial data, establishing a new bridge between time series and financial engineering. This book is well suited as a professional reference book on finance, statistics and statistical financial engineering. Readers are expected to have an undergraduate-level knowledge of statistics.

  5. Type inference for correspondence types

    DEFF Research Database (Denmark)

    Hüttel, Hans; Gordon, Andy; Hansen, Rene Rydhof

    2009-01-01

    We present a correspondence type/effect system for authenticity in a π-calculus with polarized channels, dependent pair types and effect terms and show how one may, given a process P and an a priori type environment E, generate constraints that are formulae in the Alternating Least Fixed......-Point (ALFP) logic. We then show how a reasonable model of the generated constraints yields a type/effect assignment such that P becomes well-typed with respect to E if and only if this is possible. The formulae generated satisfy a finite model property; a system of constraints is satisfiable if and only...... if it has a finite model. As a consequence, we obtain the result that type/effect inference in our system is polynomial-time decidable....

  6. Causal inference in public health.

    Science.gov (United States)

    Glass, Thomas A; Goodman, Steven N; Hernán, Miguel A; Samet, Jonathan M

    2013-01-01

    Causal inference has a central role in public health; the determination that an association is causal indicates the possibility for intervention. We review and comment on the long-used guidelines for interpreting evidence as supporting a causal association and contrast them with the potential outcomes framework that encourages thinking in terms of causes that are interventions. We argue that in public health this framework is more suitable, providing an estimate of an action's consequences rather than the less precise notion of a risk factor's causal effect. A variety of modern statistical methods adopt this approach. When an intervention cannot be specified, causal relations can still exist, but how to intervene to change the outcome will be unclear. In application, the often-complex structure of causal processes needs to be acknowledged and appropriate data collected to study them. These newer approaches need to be brought to bear on the increasingly complex public health challenges of our globalized world.

  7. Inferring Human Activity in Mobile Devices by Computing Multiple Contexts.

    Science.gov (United States)

    Chen, Ruizhi; Chu, Tianxing; Liu, Keqiang; Liu, Jingbin; Chen, Yuwei

    2015-08-28

    This paper introduces a framework for inferring human activities in mobile devices by computing spatial contexts, temporal contexts, spatiotemporal contexts, and user contexts. A spatial context is a significant location that is defined as a geofence, which can be a node associated with a circle, or a polygon; a temporal context contains time-related information that can be e.g., a local time tag, a time difference between geographical locations, or a timespan; a spatiotemporal context is defined as a dwelling length at a particular spatial context; and a user context includes user-related information that can be the user's mobility contexts, environmental contexts, psychological contexts or social contexts. Using the measurements of the built-in sensors and radio signals in mobile devices, we can snapshot a contextual tuple for every second including aforementioned contexts. Giving a contextual tuple, the framework evaluates the posteriori probability of each candidate activity in real-time using a Naïve Bayes classifier. A large dataset containing 710,436 contextual tuples has been recorded for one week from an experiment carried out at Texas A&M University Corpus Christi with three participants. The test results demonstrate that the multi-context solution significantly outperforms the spatial-context-only solution. A classification accuracy of 61.7% is achieved for the spatial-context-only solution, while 88.8% is achieved for the multi-context solution.

  8. Bacterial vaginosis - aftercare

    Science.gov (United States)

    Bacterial vaginosis (BV) is a type of vaginal infection. The vagina normally contains both healthy bacteria and unhealthy bacteria. BV occurs when more unhealthy bacteria grow than healthy bacteria. No one knows ...

  9. Bacterial surface adaptation

    Science.gov (United States)

    Utada, Andrew

    2014-03-01

    Biofilms are structured multi-cellular communities that are fundamental to the biology and ecology of bacteria. Parasitic bacterial biofilms can cause lethal infections and biofouling, but commensal bacterial biofilms, such as those found in the gut, can break down otherwise indigestible plant polysaccharides and allow us to enjoy vegetables. The first step in biofilm formation, adaptation to life on a surface, requires a working knowledge of low Reynolds number fluid physics, and the coordination of biochemical signaling, polysaccharide production, and molecular motility motors. These crucial early stages of biofilm formation are at present poorly understood. By adapting methods from soft matter physics, we dissect bacterial social behavior at the single cell level for several prototypical bacterial species, including Pseudomonas aeruginosa and Vibrio cholerae.

  10. Bacterial intermediate filaments

    DEFF Research Database (Denmark)

    Charbon, Godefroid; Cabeen, M.; Jacobs-Wagner, C.

    2009-01-01

    Crescentin, which is the founding member of a rapidly growing family of bacterial cytoskeletal proteins, was previously proposed to resemble eukaryotic intermediate filament (IF) proteins based on structural prediction and in vitro polymerization properties. Here, we demonstrate that crescentin...

  11. Diagnosis of bacterial infection

    African Journals Online (AJOL)

    direct or indirect evidence of a compatible bacterial pathogen. Inflammation may be .... cardinal features (fever, confusion, headache and neck stiffness). .... specificity, inappropriate indications or poor sampling technique may diminish this ...

  12. Spatial reconstruction of single-cell gene expression

    Science.gov (United States)

    Satija, Rahul; Farrell, Jeffrey A.; Gennert, David; Schier, Alexander F.; Regev, Aviv

    2015-01-01

    Spatial localization is a key determinant of cellular fate and behavior, but spatial RNA assays traditionally rely on staining for a limited number of RNA species. In contrast, single-cell RNA-seq allows for deep profiling of cellular gene expression, but established methods separate cells from their native spatial context. Here we present Seurat, a computational strategy to infer cellular localization by integrating single-cell RNA-seq data with in situ RNA patterns. We applied Seurat to spatially map 851 single cells from dissociated zebrafish (Danio rerio) embryos, inferring a transcriptome-wide map of spatial patterning. We confirmed Seurat’s accuracy using several experimental approaches, and used it to identify a set of archetypal expression patterns and spatial markers. Additionally, Seurat correctly localizes rare subpopulations, accurately mapping both spatially restricted and scattered groups. Seurat will be applicable to mapping cellular localization within complex patterned tissues in diverse systems. PMID:25867923

  13. Inference Attacks and Control on Database Structures

    Directory of Open Access Journals (Sweden)

    Muhamed Turkanovic

    2015-02-01

    Full Text Available Today’s databases store information with sensitivity levels that range from public to highly sensitive, hence ensuring confidentiality can be highly important, but also requires costly control. This paper focuses on the inference problem on different database structures. It presents possible treats on privacy with relation to the inference, and control methods for mitigating these treats. The paper shows that using only access control, without any inference control is inadequate, since these models are unable to protect against indirect data access. Furthermore, it covers new inference problems which rise from the dimensions of new technologies like XML, semantics, etc.

  14. Spatial Theography

    OpenAIRE

    van Noppen, Jean Pierre

    1995-01-01

    Descriptive theology («theography») frequently resorts to metaphorical modes of meaning. Among these metaphors, the spatial language of localization and orientation plays an important role to delineate tentative insights into the relationship between the human and the divine. These spatial metaphors are presumably based on the universal human experience of interaction between the body and its environment. It is dangerous, however, to postulate universal agreement on meanings associated with s...

  15. An estimating function approach to inference for inhomogeneous Neyman-Scott processes

    DEFF Research Database (Denmark)

    Waagepetersen, Rasmus Plenge

    “This paper is concerned with inference for a certain class of inhomogeneous Neyman-Scott point processes depending on spatial covariates. Regression parameter estimates obtained from a simple estimating function are shown to be asymptotically normal when the “mother” intensity for the Neyman-Scott...

  16. An estimating function approach to inference for inhomogeneous Neyman-Scott processes

    DEFF Research Database (Denmark)

    Waagepetersen, Rasmus

    2007-01-01

    This article is concerned with inference for a certain class of inhomogeneous Neyman-Scott point processes depending on spatial covariates. Regression parameter estimates obtained from a simple estimating function are shown to be asymptotically normal when the "mother" intensity for the Neyman-Sc...

  17. Inferring clocks when lacking rocks: the variable rates of molecular evolution in bacteria

    Directory of Open Access Journals (Sweden)

    Ochman Howard

    2009-09-01

    Full Text Available Abstract Background Because bacteria do not have a robust fossil record, attempts to infer the timing of events in their evolutionary history requires comparisons of molecular sequences. This use of molecular clocks is based on the assumptions that substitution rates for homologous genes or sites are fairly constant through time and across taxa. Violation of these conditions can lead to erroneous inferences and result in estimates that are off by orders of magnitude. In this study, we examine the consistency of substitution rates among a set of conserved genes in diverse bacterial lineages, and address the questions regarding the validity of molecular dating. Results By examining the evolution of 16S rRNA gene in obligate endosymbionts, which can be calibrated by the fossil record of their hosts, we found that the rates are consistent within a clade but varied widely across different bacterial lineages. Genome-wide estimates of nonsynonymous and synonymous substitutions suggest that these two measures are highly variable in their rates across bacterial taxa. Genetic drift plays a fundamental role in determining the accumulation of substitutions in 16S rRNA genes and at nonsynonymous sites. Moreover, divergence estimates based on a set of universally conserved protein-coding genes also exhibit low correspondence to those based on 16S rRNA genes. Conclusion Our results document a wide range of substitution rates across genes and bacterial taxa. This high level of variation cautions against the assumption of a universal molecular clock for inferring divergence times in bacteria. However, by applying relative-rate tests to homologous genes, it is possible to derive reliable local clocks that can be used to calibrate bacterial evolution. Reviewers This article was reviewed by Adam Eyre-Walker, Simonetta Gribaldo and Tal Pupko (nominated by Dan Graur.

  18. Spatiotemporal patterns of precipitation inferred from streamflow observations across the Sierra Nevada mountain range

    Science.gov (United States)

    Henn, Brian; Clark, Martyn P.; Kavetski, Dmitri; Newman, Andrew J.; Hughes, Mimi; McGurk, Bruce; Lundquist, Jessica D.

    2018-01-01

    Given uncertainty in precipitation gauge-based gridded datasets over complex terrain, we use multiple streamflow observations as an additional source of information about precipitation, in order to identify spatial and temporal differences between a gridded precipitation dataset and precipitation inferred from streamflow. We test whether gridded datasets capture across-crest and regional spatial patterns of variability, as well as year-to-year variability and trends in precipitation, in comparison to precipitation inferred from streamflow. We use a Bayesian model calibration routine with multiple lumped hydrologic model structures to infer the most likely basin-mean, water-year total precipitation for 56 basins with long-term (>30 year) streamflow records in the Sierra Nevada mountain range of California. We compare basin-mean precipitation derived from this approach with basin-mean precipitation from a precipitation gauge-based, 1/16° gridded dataset that has been used to simulate and evaluate trends in Western United States streamflow and snowpack over the 20th century. We find that the long-term average spatial patterns differ: in particular, there is less precipitation in the gridded dataset in higher-elevation basins whose aspect faces prevailing cool-season winds, as compared to precipitation inferred from streamflow. In a few years and basins, there is less gridded precipitation than there is observed streamflow. Lower-elevation, southern, and east-of-crest basins show better agreement between gridded and inferred precipitation. Implied actual evapotranspiration (calculated as precipitation minus streamflow) then also varies between the streamflow-based estimates and the gridded dataset. Absolute uncertainty in precipitation inferred from streamflow is substantial, but the signal of basin-to-basin and year-to-year differences are likely more robust. The findings suggest that considering streamflow when spatially distributing precipitation in complex terrain

  19. LAIT: a local ancestry inference toolkit.

    Science.gov (United States)

    Hui, Daniel; Fang, Zhou; Lin, Jerome; Duan, Qing; Li, Yun; Hu, Ming; Chen, Wei

    2017-09-06

    Inferring local ancestry in individuals of mixed ancestry has many applications, most notably in identifying disease-susceptible loci that vary among different ethnic groups. Many software packages are available for inferring local ancestry in admixed individuals. However, most of these existing software packages require specific formatted input files and generate output files in various types, yielding practical inconvenience. We developed a tool set, Local Ancestry Inference Toolkit (LAIT), which can convert standardized files into software-specific input file formats as well as standardize and summarize inference results for four popular local ancestry inference software: HAPMIX, LAMP, LAMP-LD, and ELAI. We tested LAIT using both simulated and real data sets and demonstrated that LAIT provides convenience to run multiple local ancestry inference software. In addition, we evaluated the performance of local ancestry software among different supported software packages, mainly focusing on inference accuracy and computational resources used. We provided a toolkit to facilitate the use of local ancestry inference software, especially for users with limited bioinformatics background.

  20. Generative Inferences Based on Learned Relations

    Science.gov (United States)

    Chen, Dawn; Lu, Hongjing; Holyoak, Keith J.

    2017-01-01

    A key property of relational representations is their "generativity": From partial descriptions of relations between entities, additional inferences can be drawn about other entities. A major theoretical challenge is to demonstrate how the capacity to make generative inferences could arise as a result of learning relations from…

  1. Inference in models with adaptive learning

    NARCIS (Netherlands)

    Chevillon, G.; Massmann, M.; Mavroeidis, S.

    2010-01-01

    Identification of structural parameters in models with adaptive learning can be weak, causing standard inference procedures to become unreliable. Learning also induces persistent dynamics, and this makes the distribution of estimators and test statistics non-standard. Valid inference can be

  2. Fiducial inference - A Neyman-Pearson interpretation

    NARCIS (Netherlands)

    Salome, D; VonderLinden, W; Dose,; Fischer, R; Preuss, R

    1999-01-01

    Fisher's fiducial argument is a tool for deriving inferences in the form of a probability distribution on the parameter space, not based on Bayes's Theorem. Lindley established that in exceptional situations fiducial inferences coincide with posterior distributions; in the other situations fiducial

  3. Uncertainty in prediction and in inference

    NARCIS (Netherlands)

    Hilgevoord, J.; Uffink, J.

    1991-01-01

    The concepts of uncertainty in prediction and inference are introduced and illustrated using the diffraction of light as an example. The close re-lationship between the concepts of uncertainty in inference and resolving power is noted. A general quantitative measure of uncertainty in

  4. Causal inference in economics and marketing.

    Science.gov (United States)

    Varian, Hal R

    2016-07-05

    This is an elementary introduction to causal inference in economics written for readers familiar with machine learning methods. The critical step in any causal analysis is estimating the counterfactual-a prediction of what would have happened in the absence of the treatment. The powerful techniques used in machine learning may be useful for developing better estimates of the counterfactual, potentially improving causal inference.

  5. Nonparametric predictive inference in statistical process control

    NARCIS (Netherlands)

    Arts, G.R.J.; Coolen, F.P.A.; Laan, van der P.

    2000-01-01

    New methods for statistical process control are presented, where the inferences have a nonparametric predictive nature. We consider several problems in process control in terms of uncertainties about future observable random quantities, and we develop inferences for these random quantities hased on

  6. The Impact of Disablers on Predictive Inference

    Science.gov (United States)

    Cummins, Denise Dellarosa

    2014-01-01

    People consider alternative causes when deciding whether a cause is responsible for an effect (diagnostic inference) but appear to neglect them when deciding whether an effect will occur (predictive inference). Five experiments were conducted to test a 2-part explanation of this phenomenon: namely, (a) that people interpret standard predictive…

  7. Compiling Relational Bayesian Networks for Exact Inference

    DEFF Research Database (Denmark)

    Jaeger, Manfred; Darwiche, Adnan; Chavira, Mark

    2006-01-01

    We describe in this paper a system for exact inference with relational Bayesian networks as defined in the publicly available PRIMULA tool. The system is based on compiling propositional instances of relational Bayesian networks into arithmetic circuits and then performing online inference...

  8. Compiling Relational Bayesian Networks for Exact Inference

    DEFF Research Database (Denmark)

    Jaeger, Manfred; Chavira, Mark; Darwiche, Adnan

    2004-01-01

    We describe a system for exact inference with relational Bayesian networks as defined in the publicly available \\primula\\ tool. The system is based on compiling propositional instances of relational Bayesian networks into arithmetic circuits and then performing online inference by evaluating...

  9. Extended likelihood inference in reliability

    International Nuclear Information System (INIS)

    Martz, H.F. Jr.; Beckman, R.J.; Waller, R.A.

    1978-10-01

    Extended likelihood methods of inference are developed in which subjective information in the form of a prior distribution is combined with sampling results by means of an extended likelihood function. The extended likelihood function is standardized for use in obtaining extended likelihood intervals. Extended likelihood intervals are derived for the mean of a normal distribution with known variance, the failure-rate of an exponential distribution, and the parameter of a binomial distribution. Extended second-order likelihood methods are developed and used to solve several prediction problems associated with the exponential and binomial distributions. In particular, such quantities as the next failure-time, the number of failures in a given time period, and the time required to observe a given number of failures are predicted for the exponential model with a gamma prior distribution on the failure-rate. In addition, six types of life testing experiments are considered. For the binomial model with a beta prior distribution on the probability of nonsurvival, methods are obtained for predicting the number of nonsurvivors in a given sample size and for predicting the required sample size for observing a specified number of nonsurvivors. Examples illustrate each of the methods developed. Finally, comparisons are made with Bayesian intervals in those cases where these are known to exist

  10. Reinforcement learning or active inference?

    Science.gov (United States)

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

    2009-07-29

    This paper questions the need for reinforcement learning or control theory when optimising behaviour. We show that it is fairly simple to teach an agent complicated and adaptive behaviours using a free-energy formulation of perception. In this formulation, agents adjust their internal states and sampling of the environment to minimize their free-energy. Such agents learn causal structure in the environment and sample it in an adaptive and self-supervised fashion. This results in behavioural policies that reproduce those optimised by reinforcement learning and dynamic programming. Critically, we do not need to invoke the notion of reward, value or utility. We illustrate these points by solving a benchmark problem in dynamic programming; namely the mountain-car problem, using active perception or inference under the free-energy principle. The ensuing proof-of-concept may be important because the free-energy formulation furnishes a unified account of both action and perception and may speak to a reappraisal of the role of dopamine in the brain.

  11. Reinforcement learning or active inference?

    Directory of Open Access Journals (Sweden)

    Karl J Friston

    2009-07-01

    Full Text Available This paper questions the need for reinforcement learning or control theory when optimising behaviour. We show that it is fairly simple to teach an agent complicated and adaptive behaviours using a free-energy formulation of perception. In this formulation, agents adjust their internal states and sampling of the environment to minimize their free-energy. Such agents learn causal structure in the environment and sample it in an adaptive and self-supervised fashion. This results in behavioural policies that reproduce those optimised by reinforcement learning and dynamic programming. Critically, we do not need to invoke the notion of reward, value or utility. We illustrate these points by solving a benchmark problem in dynamic programming; namely the mountain-car problem, using active perception or inference under the free-energy principle. The ensuing proof-of-concept may be important because the free-energy formulation furnishes a unified account of both action and perception and may speak to a reappraisal of the role of dopamine in the brain.

  12. Active inference and epistemic value.

    Science.gov (United States)

    Friston, Karl; Rigoli, Francesco; Ognibene, Dimitri; Mathys, Christoph; Fitzgerald, Thomas; Pezzulo, Giovanni

    2015-01-01

    We offer a formal treatment of choice behavior based on the premise that agents minimize the expected free energy of future outcomes. Crucially, the negative free energy or quality of a policy can be decomposed into extrinsic and epistemic (or intrinsic) value. Minimizing expected free energy is therefore equivalent to maximizing extrinsic value or expected utility (defined in terms of prior preferences or goals), while maximizing information gain or intrinsic value (or reducing uncertainty about the causes of valuable outcomes). The resulting scheme resolves the exploration-exploitation dilemma: Epistemic value is maximized until there is no further information gain, after which exploitation is assured through maximization of extrinsic value. This is formally consistent with the Infomax principle, generalizing formulations of active vision based upon salience (Bayesian surprise) and optimal decisions based on expected utility and risk-sensitive (Kullback-Leibler) control. Furthermore, as with previous active inference formulations of discrete (Markovian) problems, ad hoc softmax parameters become the expected (Bayes-optimal) precision of beliefs about, or confidence in, policies. This article focuses on the basic theory, illustrating the ideas with simulations. A key aspect of these simulations is the similarity between precision updates and dopaminergic discharges observed in conditioning paradigms.

  13. Bayesian inference from count data using discrete uniform priors.

    Directory of Open Access Journals (Sweden)

    Federico Comoglio

    Full Text Available We consider a set of sample counts obtained by sampling arbitrary fractions of a finite volume containing an homogeneously dispersed population of identical objects. We report a Bayesian derivation of the posterior probability distribution of the population size using a binomial likelihood and non-conjugate, discrete uniform priors under sampling with or without replacement. Our derivation yields a computationally feasible formula that can prove useful in a variety of statistical problems involving absolute quantification under uncertainty. We implemented our algorithm in the R package dupiR and compared it with a previously proposed Bayesian method based on a Gamma prior. As a showcase, we demonstrate that our inference framework can be used to estimate bacterial survival curves from measurements characterized by extremely low or zero counts and rather high sampling fractions. All in all, we provide a versatile, general purpose algorithm to infer population sizes from count data, which can find application in a broad spectrum of biological and physical problems.

  14. Bayesian Inference Methods for Sparse Channel Estimation

    DEFF Research Database (Denmark)

    Pedersen, Niels Lovmand

    2013-01-01

    This thesis deals with sparse Bayesian learning (SBL) with application to radio channel estimation. As opposed to the classical approach for sparse signal representation, we focus on the problem of inferring complex signals. Our investigations within SBL constitute the basis for the development...... of Bayesian inference algorithms for sparse channel estimation. Sparse inference methods aim at finding the sparse representation of a signal given in some overcomplete dictionary of basis vectors. Within this context, one of our main contributions to the field of SBL is a hierarchical representation...... analysis of the complex prior representation, where we show that the ability to induce sparse estimates of a given prior heavily depends on the inference method used and, interestingly, whether real or complex variables are inferred. We also show that the Bayesian estimators derived from the proposed...

  15. EI: A Program for Ecological Inference

    Directory of Open Access Journals (Sweden)

    Gary King

    2004-09-01

    Full Text Available The program EI provides a method of inferring individual behavior from aggregate data. It implements the statistical procedures, diagnostics, and graphics from the book A Solution to the Ecological Inference Problem: Reconstructing Individual Behavior from Aggregate Data (King 1997. Ecological inference, as traditionally defined, is the process of using aggregate (i.e., "ecological" data to infer discrete individual-level relationships of interest when individual-level data are not available. Ecological inferences are required in political science research when individual-level surveys are unavailable (e.g., local or comparative electoral politics, unreliable (racial politics, insufficient (political geography, or infeasible (political history. They are also required in numerous areas of ma jor significance in public policy (e.g., for applying the Voting Rights Act and other academic disciplines ranging from epidemiology and marketing to sociology and quantitative history.

  16. The constancy of gene conservation across divergent bacterial orders

    Directory of Open Access Journals (Sweden)

    Ackermann Martin

    2009-01-01

    Full Text Available Abstract Background Orthologous genes are frequently presumed to perform similar functions. However, outside of model organisms, this is rarely tested. One means of inferring changes in function is if there are changes in the level of gene conservation and selective constraint. Here we compare levels of gene conservation across three bacterial groups to test for changes in gene functionality. Findings The level of gene conservation for different orthologous genes is highly correlated across clades, even for highly divergent groups of bacteria. These correlations do not arise from broad differences in gene functionality (e.g. informational genes vs. metabolic genes, but instead seem to result from very specific differences in gene function. Furthermore, these functional differences appear to be maintained over very long periods of time. Conclusion These results suggest that even over broad time scales, most bacterial genes are under a nearly constant level of purifying selection, and that bacterial evolution is thus dominated by selective and functional stasis.

  17. Bacterial Cell Mechanics.

    Science.gov (United States)

    Auer, George K; Weibel, Douglas B

    2017-07-25

    Cellular mechanical properties play an integral role in bacterial survival and adaptation. Historically, the bacterial cell wall and, in particular, the layer of polymeric material called the peptidoglycan were the elements to which cell mechanics could be primarily attributed. Disrupting the biochemical machinery that assembles the peptidoglycan (e.g., using the β-lactam family of antibiotics) alters the structure of this material, leads to mechanical defects, and results in cell lysis. Decades after the discovery of peptidoglycan-synthesizing enzymes, the mechanisms that underlie their positioning and regulation are still not entirely understood. In addition, recent evidence suggests a diverse group of other biochemical elements influence bacterial cell mechanics, may be regulated by new cellular mechanisms, and may be triggered in different environmental contexts to enable cell adaptation and survival. This review summarizes the contributions that different biomolecular components of the cell wall (e.g., lipopolysaccharides, wall and lipoteichoic acids, lipid bilayers, peptidoglycan, and proteins) make to Gram-negative and Gram-positive bacterial cell mechanics. We discuss the contribution of individual proteins and macromolecular complexes in cell mechanics and the tools that make it possible to quantitatively decipher the biochemical machinery that contributes to bacterial cell mechanics. Advances in this area may provide insight into new biology and influence the development of antibacterial chemotherapies.

  18. Biodegradability of bacterial surfactants.

    Science.gov (United States)

    Lima, Tânia M S; Procópio, Lorena C; Brandão, Felipe D; Carvalho, André M X; Tótola, Marcos R; Borges, Arnaldo C

    2011-06-01

    This work aimed at evaluating the biodegradability of different bacterial surfactants in liquid medium and in soil microcosms. The biodegradability of biosurfactants by pure and mixed bacterial cultures was evaluated through CO(2) evolution. Three bacterial strains, Acinetobacter baumanni LBBMA ES11, Acinetobacter haemolyticus LBBMA 53 and Pseudomonas sp. LBBMA 101B, used the biosurfactants produced by Bacillus sp. LBBMA 111A (mixed lipopeptide), Bacillus subtilis LBBMA 155 (lipopeptide), Flavobacterium sp. LBBMA 168 (mixture of flavolipids), Dietzia Maris LBBMA 191(glycolipid) and Arthrobacter oxydans LBBMA 201(lipopeptide) as carbon sources in minimal medium. The synthetic surfactant sodium dodecyl sulfate (SDS) was also mineralized by these microorganisms, but at a lower rate. CO(2) emitted by a mixed bacterial culture in soil microcosms with biosurfactants was higher than in the microcosm containing SDS. Biosurfactant mineralization in soil was confirmed by the increase in surface tension of the soil aqueous extracts after incubation with the mixed bacterial culture. It can be concluded that, in terms of biodegradability and environmental security, these compounds are more suitable for applications in remediation technologies in comparison to synthetic surfactants. However, more information is needed on structure of biosurfactants, their interaction with soil and contaminants and scale up and cost for biosurfactant production.

  19. Bacterial meningitis in children

    International Nuclear Information System (INIS)

    Marji, S.

    2007-01-01

    To demonstrate the epidemiology, clinical manifestations and bacteriological profile of bacterial meningitis in children beyond the neonatal period in our hospital. This was a retrospective descriptive study conducted at Prince Rashid Hospital in Irbid, Jordan. The medical records of 50 children with the diagnosis of bacterial meningitis during 4 years period, were reviewed. The main cause of infection was streptococcus pneumoniae, followed by Haemophilus influenza and Niesseria meningitides. Mortality was higher in infants and meningococcal infection, while complications were more encountered in cases of streptococcus pneumoniae. Cerebrospinal fluid culture was positive in 11 cases and Latex agglutination test in 39. There is a significant reduction of the numbers of bacterial meningitis caused by Haemophilus influenza type B species. (author)

  20. Spatial networks

    Science.gov (United States)

    Barthélemy, Marc

    2011-02-01

    Complex systems are very often organized under the form of networks where nodes and edges are embedded in space. Transportation and mobility networks, Internet, mobile phone networks, power grids, social and contact networks, and neural networks, are all examples where space is relevant and where topology alone does not contain all the information. Characterizing and understanding the structure and the evolution of spatial networks is thus crucial for many different fields, ranging from urbanism to epidemiology. An important consequence of space on networks is that there is a cost associated with the length of edges which in turn has dramatic effects on the topological structure of these networks. We will thoroughly explain the current state of our understanding of how the spatial constraints affect the structure and properties of these networks. We will review the most recent empirical observations and the most important models of spatial networks. We will also discuss various processes which take place on these spatial networks, such as phase transitions, random walks, synchronization, navigation, resilience, and disease spread.

  1. Spatial interpolation

    NARCIS (Netherlands)

    Stein, A.

    1991-01-01

    The theory and practical application of techniques of statistical interpolation are studied in this thesis, and new developments in multivariate spatial interpolation and the design of sampling plans are discussed. Several applications to studies in soil science are

  2. Adult bacterial meningitis

    DEFF Research Database (Denmark)

    Meyer, C N; Samuelsson, I S; Galle, M

    2004-01-01

    Episodes of adult bacterial meningitis (ABM) at a Danish hospital in 1991-2000 were identified from the databases of the Department of Clinical Microbiology, and compared with data from the Danish National Patient Register and the Danish National Notification System. Reduced penicillin susceptibi......Episodes of adult bacterial meningitis (ABM) at a Danish hospital in 1991-2000 were identified from the databases of the Department of Clinical Microbiology, and compared with data from the Danish National Patient Register and the Danish National Notification System. Reduced penicillin...

  3. Statistical inference an integrated Bayesianlikelihood approach

    CERN Document Server

    Aitkin, Murray

    2010-01-01

    Filling a gap in current Bayesian theory, Statistical Inference: An Integrated Bayesian/Likelihood Approach presents a unified Bayesian treatment of parameter inference and model comparisons that can be used with simple diffuse prior specifications. This novel approach provides new solutions to difficult model comparison problems and offers direct Bayesian counterparts of frequentist t-tests and other standard statistical methods for hypothesis testing.After an overview of the competing theories of statistical inference, the book introduces the Bayes/likelihood approach used throughout. It pre

  4. Predicted Bacterial Interactions Affect in Vivo Microbial Colonization Dynamics in Nematostella

    Science.gov (United States)

    Domin, Hanna; Zurita-Gutiérrez, Yazmín H.; Scotti, Marco; Buttlar, Jann; Hentschel Humeida, Ute; Fraune, Sebastian

    2018-01-01

    The maintenance and resilience of host-associated microbiota during development is a fundamental process influencing the fitness of many organisms. Several host properties were identified as influencing factors on bacterial colonization, including the innate immune system, mucus composition, and diet. In contrast, the importance of bacteria–bacteria interactions on host colonization is less understood. Here, we use bacterial abundance data of the marine model organism Nematostella vectensis to reconstruct potential bacteria–bacteria interactions through co-occurrence networks. The analysis indicates that bacteria–bacteria interactions are dynamic during host colonization and change according to the host’s developmental stage. To assess the predictive power of inferred interactions, we tested bacterial isolates with predicted cooperative or competitive behavior for their ability to influence bacterial recolonization dynamics. Within 3 days of recolonization, all tested bacterial isolates affected bacterial community structure, while only competitive bacteria increased bacterial diversity. Only 1 week after recolonization, almost no differences in bacterial community structure could be observed between control and treatments. These results show that predicted competitive bacteria can influence community structure for a short period of time, verifying the in silico predictions. However, within 1 week, the effects of the bacterial isolates are neutralized, indicating a high degree of resilience of the bacterial community. PMID:29740401

  5. Predicted Bacterial Interactions Affect in Vivo Microbial Colonization Dynamics in Nematostella

    Directory of Open Access Journals (Sweden)

    Hanna Domin

    2018-04-01

    Full Text Available The maintenance and resilience of host-associated microbiota during development is a fundamental process influencing the fitness of many organisms. Several host properties were identified as influencing factors on bacterial colonization, including the innate immune system, mucus composition, and diet. In contrast, the importance of bacteria–bacteria interactions on host colonization is less understood. Here, we use bacterial abundance data of the marine model organism Nematostella vectensis to reconstruct potential bacteria–bacteria interactions through co-occurrence networks. The analysis indicates that bacteria–bacteria interactions are dynamic during host colonization and change according to the host’s developmental stage. To assess the predictive power of inferred interactions, we tested bacterial isolates with predicted cooperative or competitive behavior for their ability to influence bacterial recolonization dynamics. Within 3 days of recolonization, all tested bacterial isolates affected bacterial community structure, while only competitive bacteria increased bacterial diversity. Only 1 week after recolonization, almost no differences in bacterial community structure could be observed between control and treatments. These results show that predicted competitive bacteria can influence community structure for a short period of time, verifying the in silico predictions. However, within 1 week, the effects of the bacterial isolates are neutralized, indicating a high degree of resilience of the bacterial community.

  6. Mapping and predictive variations of soil bacterial richness across France.

    Directory of Open Access Journals (Sweden)

    Sébastien Terrat

    Full Text Available Although numerous studies have demonstrated the key role of bacterial diversity in soil functions and ecosystem services, little is known about the variations and determinants of such diversity on a nationwide scale. The overall objectives of this study were i to describe the bacterial taxonomic richness variations across France, ii to identify the ecological processes (i.e. selection by the environment and dispersal limitation influencing this distribution, and iii to develop a statistical predictive model of soil bacterial richness. We used the French Soil Quality Monitoring Network (RMQS, which covers all of France with 2,173 sites. The soil bacterial richness (i.e. OTU number was determined by pyrosequencing 16S rRNA genes and related to the soil characteristics, climatic conditions, geomorphology, land use and space. Mapping of bacterial richness revealed a heterogeneous spatial distribution, structured into patches of about 111km, where the main drivers were the soil physico-chemical properties (18% of explained variance, the spatial descriptors (5.25%, 1.89% and 1.02% for the fine, medium and coarse scales, respectively, and the land use (1.4%. Based on these drivers, a predictive model was developed, which allows a good prediction of the bacterial richness (R2adj of 0.56 and provides a reference value for a given pedoclimatic condition.

  7. Mapping and predictive variations of soil bacterial richness across France.

    Science.gov (United States)

    Terrat, Sébastien; Horrigue, Walid; Dequiedt, Samuel; Saby, Nicolas P A; Lelièvre, Mélanie; Nowak, Virginie; Tripied, Julie; Régnier, Tiffanie; Jolivet, Claudy; Arrouays, Dominique; Wincker, Patrick; Cruaud, Corinne; Karimi, Battle; Bispo, Antonio; Maron, Pierre Alain; Chemidlin Prévost-Bouré, Nicolas; Ranjard, Lionel

    2017-01-01

    Although numerous studies have demonstrated the key role of bacterial diversity in soil functions and ecosystem services, little is known about the variations and determinants of such diversity on a nationwide scale. The overall objectives of this study were i) to describe the bacterial taxonomic richness variations across France, ii) to identify the ecological processes (i.e. selection by the environment and dispersal limitation) influencing this distribution, and iii) to develop a statistical predictive model of soil bacterial richness. We used the French Soil Quality Monitoring Network (RMQS), which covers all of France with 2,173 sites. The soil bacterial richness (i.e. OTU number) was determined by pyrosequencing 16S rRNA genes and related to the soil characteristics, climatic conditions, geomorphology, land use and space. Mapping of bacterial richness revealed a heterogeneous spatial distribution, structured into patches of about 111km, where the main drivers were the soil physico-chemical properties (18% of explained variance), the spatial descriptors (5.25%, 1.89% and 1.02% for the fine, medium and coarse scales, respectively), and the land use (1.4%). Based on these drivers, a predictive model was developed, which allows a good prediction of the bacterial richness (R2adj of 0.56) and provides a reference value for a given pedoclimatic condition.

  8. Polynomial Chaos Acceleration for the Bayesian Inference of Random Fields with Gaussian Priors and Uncertain Covariance Hyper-Parameters

    KAUST Repository

    Le Maitre, Olivier

    2015-01-07

    We address model dimensionality reduction in the Bayesian inference of Gaussian fields, considering prior covariance function with unknown hyper-parameters. The Karhunen-Loeve (KL) expansion of a prior Gaussian process is traditionally derived assuming fixed covariance function with pre-assigned hyperparameter values. Thus, the modes strengths of the Karhunen-Loeve expansion inferred using available observations, as well as the resulting inferred process, dependent on the pre-assigned values for the covariance hyper-parameters. Here, we seek to infer the process and its the covariance hyper-parameters in a single Bayesian inference. To this end, the uncertainty in the hyper-parameters is treated by means of a coordinate transformation, leading to a KL-type expansion on a fixed reference basis of spatial modes, but with random coordinates conditioned on the hyper-parameters. A Polynomial Chaos (PC) expansion of the model prediction is also introduced to accelerate the Bayesian inference and the sampling of the posterior distribution with MCMC method. The PC expansion of the model prediction also rely on a coordinates transformation, enabling us to avoid expanding the dependence of the prediction with respect to the covariance hyper-parameters. We demonstrate the efficiency of the proposed method on a transient diffusion equation by inferring spatially-varying log-diffusivity fields from noisy data.

  9. [Bacterial biofilms and infection].

    Science.gov (United States)

    Lasa, I; Del Pozo, J L; Penadés, J R; Leiva, J

    2005-01-01

    In developed countries we tend to think of heart disease and the numerous forms of cancer as the main causes of mortality, but on a global scale infectious diseases come close, or may even be ahead: 14.9 million deaths in 2002 compared to cardiovascular diseases (16.9 million deaths) and cancer (7.1 million deaths) (WHO report 2004). The infectious agents responsible for human mortality have evolved as medical techniques and hygienic measures have changed. Modern-day acute infectious diseases caused by specialized bacterial pathogens such as diphtheria, tetanus, cholera, plague, which represented the main causes of death at the beginning of XX century, have been effectively controlled with antibiotics and vaccines. In their place, more than half of the infectious diseases that affect mildly immunocompromised patients involve bacterial species that are commensal with the human body; these can produce chronic infections, are resistant to antimicrobial agents and there is no effective vaccine against them. Examples of these infections are the otitis media, native valve endocarditis, chronic urinary infections, bacterial prostatitis, osteomyelitis and all the infections related to medical devices. Direct analysis of the surface of medical devices or of tissues that have been foci of chronic infections shows the presence of large numbers of bacteria surrounded by an exopolysaccharide matrix, which has been named the "biofilm". Inside the biofilm, bacteria grow protected from the action of the antibodies, phagocytic cells and antimicrobial treatments. In this article, we describe the role of bacterial biofilms in human persistent infections.

  10. Interfering with bacterial gossip

    DEFF Research Database (Denmark)

    Bjarnsholt, Thomas; Tolker-Nielsen, Tim; Givskov, Michael

    2011-01-01

    that appropriately target bacteria in their relevant habitat with the aim of mitigating their destructive impact on patients. In this review we describe molecular mechanisms involved in “bacterial gossip” (more scientifically referred to as quorum sensing (QS) and c-di-GMP signaling), virulence, biofilm formation...

  11. Bacterial fingerprints across Europe

    NARCIS (Netherlands)

    Glasner, Corinna

    2014-01-01

    Bacterial pathogens, such as Staphylococcus aureus and carbapenemase-producing Enterobacteriaceae (CPE), impose major threats to human health worldwide. Both have a ‘Jekyll & Hyde’ character, since they can be present as human commensals, but can also become harmful invasive pathogens especially

  12. Bacterial membrane proteomics.

    Science.gov (United States)

    Poetsch, Ansgar; Wolters, Dirk

    2008-10-01

    About one quarter to one third of all bacterial genes encode proteins of the inner or outer bacterial membrane. These proteins perform essential physiological functions, such as the import or export of metabolites, the homeostasis of metal ions, the extrusion of toxic substances or antibiotics, and the generation or conversion of energy. The last years have witnessed completion of a plethora of whole-genome sequences of bacteria important for biotechnology or medicine, which is the foundation for proteome and other functional genome analyses. In this review, we discuss the challenges in membrane proteome analysis, starting from sample preparation and leading to MS-data analysis and quantification. The current state of available proteomics technologies as well as their advantages and disadvantages will be described with a focus on shotgun proteomics. Then, we will briefly introduce the most abundant proteins and protein families present in bacterial membranes before bacterial membrane proteomics studies of the last years will be presented. It will be shown how these works enlarged our knowledge about the physiological adaptations that take place in bacteria during fine chemical production, bioremediation, protein overexpression, and during infections. Furthermore, several examples from literature demonstrate the suitability of membrane proteomics for the identification of antigens and different pathogenic strains, as well as the elucidation of membrane protein structure and function.

  13. [Spontaneous bacterial peritonitis].

    Science.gov (United States)

    Velkey, Bálint; Vitális, Eszter; Vitális, Zsuzsanna

    2017-01-01

    Spontaneous bacterial peritonitis occurs most commonly in cirrhotic patients with ascites. Pathogens get into the circulation by intestinal translocation and colonize in peritoneal fluid. Diagnosis of spontaneous bacterial peritonitis is based on elevated polymorphonuclear leukocyte count in the ascites (>0,25 G/L). Ascites culture is often negative but aids to get information about antibiotic sensitivity in positive cases. Treatment in stable patient can be intravenous then orally administrated ciprofloxacin or amoxicillin/clavulanic acid, while in severe cases intravenous III. generation cephalosporin. Nosocomial spontaneous bacterial peritonitis often caused by Gram-positive bacteria and multi-resistant pathogens can also be expected thus carbapenem should be the choice of the empiric treatment. Antibiotic prophylaxis should be considered. Norfloxacin is used most commonly, but changes are expected due to increase in quinolone resistance. As a primary prophylaxis, a short-term antibiotic treatment is recommended after gastrointestinal bleeding for 5 days, while long-term prophylaxis is for patients with low ascites protein, and advanced disease (400 mg/day). Secondary prophylaxis is recommended for all patients recovered from spontaneous bacterial peritonitis. Due to increasing antibiotic use of antibiotics prophylaxis is debated to some degree. Orv. Hetil., 2017, 158(2), 50-57.

  14. Inferring climate variability from skewed proxy records

    Science.gov (United States)

    Emile-Geay, J.; Tingley, M.

    2013-12-01

    compared to other proxy records. (2) a multiproxy reconstruction of temperature over the Common Era (Mann et al., 2009), where we find that about one third of the records display significant departures from normality. Accordingly, accounting for skewness in proxy predictors has a notable influence on both reconstructed global mean and spatial patterns of temperature change. Inferring climate variability from skewed proxy records thus requires cares, but can be done with relatively simple tools. References - Mann, M. E., Z. Zhang, S. Rutherford, R. S. Bradley, M. K. Hughes, D. Shindell, C. Ammann, G. Faluvegi, and F. Ni (2009), Global signatures and dynamical origins of the little ice age and medieval climate anomaly, Science, 326(5957), 1256-1260, doi:10.1126/science.1177303. - Moy, C., G. Seltzer, D. Rodbell, and D. Anderson (2002), Variability of El Niño/Southern Oscillation activ- ity at millennial timescales during the Holocene epoch, Nature, 420(6912), 162-165.

  15. Estimating mountain basin-mean precipitation from streamflow using Bayesian inference

    Science.gov (United States)

    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.

  16. Interplay of Bacterial Interactions and Spatial Organisation in Multispecies Biofilms

    DEFF Research Database (Denmark)

    Liu, Wenzheng

    -tispecies biofilms, which provides theoretical and practical arguments for further ad-vancement of this field. Here, a reproducible four-species biofilm, composed of Stenotrophomonas rhizophila, Xan-thomonas retroflexus, Microbacterium oxydans and Paenibacillus amylolyticus, was established to study the effect...

  17. Spatial Computing in Synthetic Bioware: Creating Bacterial Architectures

    OpenAIRE

    Pascalie , Jonathan; Potier , Martin; Kowaliw , Taras; Giavitto , Jean-Louis; Michel , Olivier; Spicher , Antoine; Doursat , René

    2015-01-01

    International audience; Synthetic biology is an emerging scientific field that promotes the standardized manufacturing of biological components without natural equivalents. Its goal is to create artificial living systems that can meet various needs in health care, nanotechnology and energy. Most works are currently focused on the individual bacterium as a chemical reactor. Our project, SynBioTIC, addresses a novel and more complex challenge: shape engineering, i.e. the redesign of natural mor...

  18. Bayesian Spatial Modelling with R-INLA

    Directory of Open Access Journals (Sweden)

    Finn Lindgren

    2015-02-01

    Full Text Available The principles behind the interface to continuous domain spatial models in the R- INLA software package for R are described. The integrated nested Laplace approximation (INLA approach proposed by Rue, Martino, and Chopin (2009 is a computationally effective alternative to MCMC for Bayesian inference. INLA is designed for latent Gaussian models, a very wide and flexible class of models ranging from (generalized linear mixed to spatial and spatio-temporal models. Combined with the stochastic partial differential equation approach (SPDE, Lindgren, Rue, and Lindstrm 2011, one can accommodate all kinds of geographically referenced data, including areal and geostatistical ones, as well as spatial point process data. The implementation interface covers stationary spatial mod- els, non-stationary spatial models, and also spatio-temporal models, and is applicable in epidemiology, ecology, environmental risk assessment, as well as general geostatistics.

  19. Solving the Problem of Comparing Whole Bacterial Genomes across Different Sequencing Platforms

    DEFF Research Database (Denmark)

    Kaas, Rolf Sommer; Leekitcharoenphon, Pimlapas; Aarestrup, Frank Møller

    2014-01-01

    technology because each technology has a systematic bias making integration of data generated from different platforms difficult. We developed two different procedures for identifying variable sites and inferring phylogenies in WGS data across multiple platforms. The methods were evaluated on three bacterial...

  20. Spatial distribution

    DEFF Research Database (Denmark)

    Borregaard, Michael Krabbe; Hendrichsen, Ditte Katrine; Nachman, Gøsta Støger

    2008-01-01

    , depending on the nature of intraspecific interactions between them: while the individuals of some species repel each other and partition the available area, others form groups of varying size, determined by the fitness of each group member. The spatial distribution pattern of individuals again strongly......Living organisms are distributed over the entire surface of the planet. The distribution of the individuals of each species is not random; on the contrary, they are strongly dependent on the biology and ecology of the species, and vary over different spatial scale. The structure of whole...... populations reflects the location and fragmentation pattern of the habitat types preferred by the species, and the complex dynamics of migration, colonization, and population growth taking place over the landscape. Within these, individuals are distributed among each other in regular or clumped patterns...

  1. Spatial Culture

    DEFF Research Database (Denmark)

    Reeh, Henrik

    2012-01-01

    Spatial Culture – A Humanities Perspective Abstract of introductory essay by Henrik Reeh Secured by alliances between socio-political development and cultural practices, a new field of humanistic studies in spatial culture has developed since the 1990s. To focus on links between urban culture...... and modern society is, however, an intellectual practice which has a much longer history. Already in the 1980s, the debate on the modern and the postmodern cited Paris and Los Angeles as spatio-cultural illustrations of these major philosophical concepts. Earlier, in the history of critical studies, the work...... Foucault considered a constitutive feature of 20th-century thinking and one that continues to occupy intellectual and cultural debates in the third millennium. A conceptual framework is, nevertheless, necessary, if the humanities are to adequa-tely address city and space – themes that have long been...

  2. Corticosteroids for Bacterial Keratitis

    Science.gov (United States)

    Srinivasan, Muthiah; Mascarenhas, Jeena; Rajaraman, Revathi; Ravindran, Meenakshi; Lalitha, Prajna; Glidden, David V.; Ray, Kathryn J.; Hong, Kevin C.; Oldenburg, Catherine E.; Lee, Salena M.; Zegans, Michael E.; McLeod, Stephen D.; Lietman, Thomas M.; Acharya, Nisha R.

    2013-01-01

    Objective To determine whether there is a benefit in clinical outcomes with the use of topical corticosteroids as adjunctive therapy in the treatment of bacterial corneal ulcers. Methods Randomized, placebo-controlled, double-masked, multicenter clinical trial comparing prednisolone sodium phosphate, 1.0%, to placebo as adjunctive therapy for the treatment of bacterial corneal ulcers. Eligible patients had a culture-positive bacterial corneal ulcer and received topical moxifloxacin for at least 48 hours before randomization. Main Outcome Measures The primary outcome was best spectacle-corrected visual acuity (BSCVA) at 3 months from enrollment. Secondary outcomes included infiltrate/scar size, reepithelialization, and corneal perforation. Results Between September 1, 2006, and February 22, 2010, 1769 patients were screened for the trial and 500 patients were enrolled. No significant difference was observed in the 3-month BSCVA (−0.009 logarithm of the minimum angle of resolution [logMAR]; 95% CI, −0.085 to 0.068; P = .82), infiltrate/scar size (P = .40), time to reepithelialization (P = .44), or corneal perforation (P > .99). A significant effect of corticosteroids was observed in subgroups of baseline BSCVA (P = .03) and ulcer location (P = .04). At 3 months, patients with vision of counting fingers or worse at baseline had 0.17 logMAR better visual acuity with corticosteroids (95% CI, −0.31 to −0.02; P = .03) compared with placebo, and patients with ulcers that were completely central at baseline had 0.20 logMAR better visual acuity with corticosteroids (−0.37 to −0.04; P = .02). Conclusions We found no overall difference in 3-month BSCVA and no safety concerns with adjunctive corticosteroid therapy for bacterial corneal ulcers. Application to Clinical Practice Adjunctive topical corticosteroid use does not improve 3-month vision in patients with bacterial corneal ulcers. PMID:21987582

  3. Inferring Domain Plans in Question-Answering

    National Research Council Canada - National Science Library

    Pollack, Martha E

    1986-01-01

    The importance of plan inference in models of conversation has been widely noted in the computational-linguistics literature, and its incorporation in question-answering systems has enabled a range...

  4. Scalable inference for stochastic block models

    KAUST Repository

    Peng, Chengbin; Zhang, Zhihua; Wong, Ka-Chun; Zhang, Xiangliang; Keyes, David E.

    2017-01-01

    Community detection in graphs is widely used in social and biological networks, and the stochastic block model is a powerful probabilistic tool for describing graphs with community structures. However, in the era of "big data," traditional inference

  5. Efficient algorithms for conditional independence inference

    Czech Academy of Sciences Publication Activity Database

    Bouckaert, R.; Hemmecke, R.; Lindner, S.; Studený, Milan

    2010-01-01

    Roč. 11, č. 1 (2010), s. 3453-3479 ISSN 1532-4435 R&D Projects: GA ČR GA201/08/0539; GA MŠk 1M0572 Institutional research plan: CEZ:AV0Z10750506 Keywords : conditional independence inference * linear programming approach Subject RIV: BA - General Mathematics Impact factor: 2.949, year: 2010 http://library.utia.cas.cz/separaty/2010/MTR/studeny-efficient algorithms for conditional independence inference.pdf

  6. Bacterial infec tions in travellers

    African Journals Online (AJOL)

    namely bacterial causes of travellers' diarrhoea and skin infections, as well as .... Vaccination: protective efficacy against typhoid may be overcome by ingesting a high bacterial load. Vaccine ..... preparation such as cream sauce. Only after ...

  7. On the criticality of inferred models

    Science.gov (United States)

    Mastromatteo, Iacopo; Marsili, Matteo

    2011-10-01

    Advanced inference techniques allow one to reconstruct a pattern of interaction from high dimensional data sets, from probing simultaneously thousands of units of extended systems—such as cells, neural tissues and financial markets. We focus here on the statistical properties of inferred models and argue that inference procedures are likely to yield models which are close to singular values of parameters, akin to critical points in physics where phase transitions occur. These are points where the response of physical systems to external perturbations, as measured by the susceptibility, is very large and diverges in the limit of infinite size. We show that the reparameterization invariant metrics in the space of probability distributions of these models (the Fisher information) are directly related to the susceptibility of the inferred model. As a result, distinguishable models tend to accumulate close to critical points, where the susceptibility diverges in infinite systems. This region is the one where the estimate of inferred parameters is most stable. In order to illustrate these points, we discuss inference of interacting point processes with application to financial data and show that sensible choices of observation time scales naturally yield models which are close to criticality.

  8. On the criticality of inferred models

    International Nuclear Information System (INIS)

    Mastromatteo, Iacopo; Marsili, Matteo

    2011-01-01

    Advanced inference techniques allow one to reconstruct a pattern of interaction from high dimensional data sets, from probing simultaneously thousands of units of extended systems—such as cells, neural tissues and financial markets. We focus here on the statistical properties of inferred models and argue that inference procedures are likely to yield models which are close to singular values of parameters, akin to critical points in physics where phase transitions occur. These are points where the response of physical systems to external perturbations, as measured by the susceptibility, is very large and diverges in the limit of infinite size. We show that the reparameterization invariant metrics in the space of probability distributions of these models (the Fisher information) are directly related to the susceptibility of the inferred model. As a result, distinguishable models tend to accumulate close to critical points, where the susceptibility diverges in infinite systems. This region is the one where the estimate of inferred parameters is most stable. In order to illustrate these points, we discuss inference of interacting point processes with application to financial data and show that sensible choices of observation time scales naturally yield models which are close to criticality

  9. Polynomial Chaos Surrogates for Bayesian Inference

    KAUST Repository

    Le Maitre, Olivier

    2016-01-06

    The Bayesian inference is a popular probabilistic method to solve inverse problems, such as the identification of field parameter in a PDE model. The inference rely on the Bayes rule to update the prior density of the sought field, from observations, and derive its posterior distribution. In most cases the posterior distribution has no explicit form and has to be sampled, for instance using a Markov-Chain Monte Carlo method. In practice the prior field parameter is decomposed and truncated (e.g. by means of Karhunen- Lo´eve decomposition) to recast the inference problem into the inference of a finite number of coordinates. Although proved effective in many situations, the Bayesian inference as sketched above faces several difficulties requiring improvements. First, sampling the posterior can be a extremely costly task as it requires multiple resolutions of the PDE model for different values of the field parameter. Second, when the observations are not very much informative, the inferred parameter field can highly depends on its prior which can be somehow arbitrary. These issues have motivated the introduction of reduced modeling or surrogates for the (approximate) determination of the parametrized PDE solution and hyperparameters in the description of the prior field. Our contribution focuses on recent developments in these two directions: the acceleration of the posterior sampling by means of Polynomial Chaos expansions and the efficient treatment of parametrized covariance functions for the prior field. We also discuss the possibility of making such approach adaptive to further improve its efficiency.

  10. Morphomechanics of bacterial biofilms undergoing anisotropic differential growth

    Science.gov (United States)

    Zhang, Cheng; Li, Bo; Huang, Xiao; Ni, Yong; Feng, Xi-Qiao

    2016-10-01

    Growing bacterial biofilms exhibit a number of surface morphologies, e.g., concentric wrinkles, radial ridges, and labyrinthine networks, depending on their physiological status and nutrient access. We explore the mechanisms underlying the emergence of these greatly different morphologies. Ginzburg-Landau kinetic method and Fourier spectral method are integrated to simulate the morphological evolution of bacterial biofilms. It is shown that the morphological instability of biofilms is triggered by the stresses induced by anisotropic and heterogeneous bacterial expansion, and involves the competition between membrane energy and bending energy. Local interfacial delamination further enriches the morphologies of biofilms. Phase diagrams are established to reveal how the anisotropy and spatial heterogeneity of growth modulate the surface patterns. The mechanics of three-dimensional microbial morphogenesis may also underpin self-organization in other development systems and provide a potential strategy for engineering microscopic structures from bacterial aggregates.

  11. A Bayesian Network Schema for Lessening Database Inference

    National Research Council Canada - National Science Library

    Chang, LiWu; Moskowitz, Ira S

    2001-01-01

    .... The authors introduce a formal schema for database inference analysis, based upon a Bayesian network structure, which identifies critical parameters involved in the inference problem and represents...

  12. Coordinate transformation and Polynomial Chaos for the Bayesian inference of a Gaussian process with parametrized prior covariance function

    KAUST Repository

    Sraj, Ihab

    2015-10-22

    This paper addresses model dimensionality reduction for Bayesian inference based on prior Gaussian fields with uncertainty in the covariance function hyper-parameters. The dimensionality reduction is traditionally achieved using the Karhunen-Loève expansion of a prior Gaussian process assuming covariance function with fixed hyper-parameters, despite the fact that these are uncertain in nature. The posterior distribution of the Karhunen-Loève coordinates is then inferred using available observations. The resulting inferred field is therefore dependent on the assumed hyper-parameters. Here, we seek to efficiently estimate both the field and covariance hyper-parameters using Bayesian inference. To this end, a generalized Karhunen-Loève expansion is derived using a coordinate transformation to account for the dependence with respect to the covariance hyper-parameters. Polynomial Chaos expansions are employed for the acceleration of the Bayesian inference using similar coordinate transformations, enabling us to avoid expanding explicitly the solution dependence on the uncertain hyper-parameters. We demonstrate the feasibility of the proposed method on a transient diffusion equation by inferring spatially-varying log-diffusivity fields from noisy data. The inferred profiles were found closer to the true profiles when including the hyper-parameters’ uncertainty in the inference formulation.

  13. Bacterial carbon utilization in vertical subsurface flow constructed wetlands.

    Science.gov (United States)

    Tietz, Alexandra; Langergraber, Günter; Watzinger, Andrea; Haberl, Raimund; Kirschner, Alexander K T

    2008-03-01

    Subsurface vertical flow constructed wetlands with intermittent loading are considered as state of the art and can comply with stringent effluent requirements. It is usually assumed that microbial activity in the filter body of constructed wetlands, responsible for the removal of carbon and nitrogen, relies mainly on bacterially mediated transformations. However, little quantitative information is available on the distribution of bacterial biomass and production in the "black-box" constructed wetland. The spatial distribution of bacterial carbon utilization, based on bacterial (14)C-leucine incorporation measurements, was investigated for the filter body of planted and unplanted indoor pilot-scale constructed wetlands, as well as for a planted outdoor constructed wetland. A simple mass-balance approach was applied to explain the bacterially catalysed organic matter degradation in this system by comparing estimated bacterial carbon utilization rates with simultaneously measured carbon reduction values. The pilot-scale constructed wetlands proved to be a suitable model system for investigating microbial carbon utilization in constructed wetlands. Under an ideal operating mode, the bulk of bacterial productivity occurred within the first 10cm of the filter body. Plants seemed to have no significant influence on productivity and biomass of bacteria, as well as on wastewater total organic carbon removal.

  14. Inferring the palaeoenvironment of ancient bacteria on the basis of resurrected proteins

    Science.gov (United States)

    Gaucher, Eric A.; Thomson, J. Michael; Burgan, Michelle F.; Benner, Steven A.

    2003-01-01

    Features of the physical environment surrounding an ancestral organism can be inferred by reconstructing sequences of ancient proteins made by those organisms, resurrecting these proteins in the laboratory, and measuring their properties. Here, we resurrect candidate sequences for elongation factors of the Tu family (EF-Tu) found at ancient nodes in the bacterial evolutionary tree, and measure their activities as a function of temperature. The ancient EF-Tu proteins have temperature optima of 55-65 degrees C. This value seems to be robust with respect to uncertainties in the ancestral reconstruction. This suggests that the ancient bacteria that hosted these particular genes were thermophiles, and neither hyperthermophiles nor mesophiles. This conclusion can be compared and contrasted with inferences drawn from an analysis of the lengths of branches in trees joining proteins from contemporary bacteria, the distribution of thermophily in derived bacterial lineages, the inferred G + C content of ancient ribosomal RNA, and the geological record combined with assumptions concerning molecular clocks. The study illustrates the use of experimental palaeobiochemistry and assumptions about deep phylogenetic relationships between bacteria to explore the character of ancient life.

  15. Structure of bacterial lipopolysaccharides.

    Science.gov (United States)

    Caroff, Martine; Karibian, Doris

    2003-11-14

    Bacterial lipopolysaccharides are the major components of the outer surface of Gram-negative bacteria They are often of interest in medicine for their immunomodulatory properties. In small amounts they can be beneficial, but in larger amounts they may cause endotoxic shock. Although they share a common architecture, their structural details exert a strong influence on their activity. These molecules comprise: a lipid moiety, called lipid A, which is considered to be the endotoxic component, a glycosidic part consisting of a core of approximately 10 monosaccharides and, in "smooth-type" lipopolysaccharides, a third region, named O-chain, consisting of repetitive subunits of one to eight monosaccharides responsible for much of the immunospecificity of the bacterial cell.

  16. Cooperative Bacterial Foraging Optimization

    Directory of Open Access Journals (Sweden)

    Hanning Chen

    2009-01-01

    Full Text Available Bacterial Foraging Optimization (BFO is a novel optimization algorithm based on the social foraging behavior of E. coli bacteria. This paper presents a variation on the original BFO algorithm, namely, the Cooperative Bacterial Foraging Optimization (CBFO, which significantly improve the original BFO in solving complex optimization problems. This significant improvement is achieved by applying two cooperative approaches to the original BFO, namely, the serial heterogeneous cooperation on the implicit space decomposition level and the serial heterogeneous cooperation on the hybrid space decomposition level. The experiments compare the performance of two CBFO variants with the original BFO, the standard PSO and a real-coded GA on four widely used benchmark functions. The new method shows a marked improvement in performance over the original BFO and appears to be comparable with the PSO and GA.

  17. Bacterial control of cyanobacteria

    CSIR Research Space (South Africa)

    Ndlela, Luyanda L

    2017-08-01

    Full Text Available of biological control appears to be direct contact. • Ndlela, L. L. et al. (2016) ‘An overview of cyanobacterial bloom occurrences and research in Africa over the last decade’, Harmful Algae, 60 • Gumbo, J.R. et al. (2010) The Isolation and identification... of Predatory Bacteria from a Microcystis algal Bloom.. African Journal of Biotechnology, 9. *Special acknowledgement goes to the National Research foundation for funding this presentation Bacterial control of cyanobacteria Luyanda...

  18. Statistical Models for Inferring Vegetation Composition from Fossil Pollen

    Science.gov (United States)

    Paciorek, C.; McLachlan, J. S.; Shang, Z.

    2011-12-01

    Fossil pollen provide information about vegetation composition that can be used to help understand how vegetation has changed over the past. However, these data have not traditionally been analyzed in a way that allows for statistical inference about spatio-temporal patterns and trends. We build a Bayesian hierarchical model called STEPPS (Spatio-Temporal Empirical Prediction from Pollen in Sediments) that predicts forest composition in southern New England, USA, over the last two millenia based on fossil pollen. The critical relationships between abundances of tree taxa in the pollen record and abundances in actual vegetation are estimated using modern (Forest Inventory Analysis) data and (witness tree) data from colonial records. This gives us two time points at which both pollen and direct vegetation data are available. Based on these relationships, and incorporating our uncertainty about them, we predict forest composition using fossil pollen. We estimate the spatial distribution and relative abundances of tree species and draw inference about how these patterns have changed over time. Finally, we describe ongoing work to extend the modeling to the upper Midwest of the U.S., including an approach to infer tree density and thereby estimate the prairie-forest boundary in Minnesota and Wisconsin. This work is part of the PalEON project, which brings together a team of ecosystem modelers, paleoecologists, and statisticians with the goal of reconstructing vegetation responses to climate during the last two millenia in the northeastern and midwestern United States. The estimates from the statistical modeling will be used to assess and calibrate ecosystem models that are used to project ecological changes in response to global change.

  19. Bacterial growth kinetics

    International Nuclear Information System (INIS)

    Boonkitticharoen, V.; Ehrhardt, J.C.; Kirchner, P.T.

    1989-01-01

    Quantitative measurement of bacterial growth may be made using a radioassay technique. This method measures, by scintillation counting, the 14 CO 2 derived from the bacterial metabolism of a 14 C-labeled substrate. Mathematical growth models may serve as reliable tools for estimation of the generation rate constant (or slope of the growth curve) and provide a basis for evaluating assay performance. Two models, i.e., exponential and logistic, are proposed. Both models yielded an accurate fit to the data from radioactive measurement of bacterial growth. The exponential model yielded high precision values of the generation rate constant, with an average relative standard deviation of 1.2%. Under most conditions the assay demonstrated no changes in the slopes of growth curves when the number of bacteria per inoculation was changed. However, the radiometric assay by scintillation method had a growth-inhibiting effect on a few strains of bacteria. The source of this problem was thought to be hypersensitivity to trace amounts of toluene remaining on the detector

  20. Adaptive Bacterial Foraging Optimization

    Directory of Open Access Journals (Sweden)

    Hanning Chen

    2011-01-01

    Full Text Available Bacterial Foraging Optimization (BFO is a recently developed nature-inspired optimization algorithm, which is based on the foraging behavior of E. coli bacteria. Up to now, BFO has been applied successfully to some engineering problems due to its simplicity and ease of implementation. However, BFO possesses a poor convergence behavior over complex optimization problems as compared to other nature-inspired optimization techniques. This paper first analyzes how the run-length unit parameter of BFO controls the exploration of the whole search space and the exploitation of the promising areas. Then it presents a variation on the original BFO, called the adaptive bacterial foraging optimization (ABFO, employing the adaptive foraging strategies to improve the performance of the original BFO. This improvement is achieved by enabling the bacterial foraging algorithm to adjust the run-length unit parameter dynamically during algorithm execution in order to balance the exploration/exploitation tradeoff. The experiments compare the performance of two versions of ABFO with the original BFO, the standard particle swarm optimization (PSO and a real-coded genetic algorithm (GA on four widely-used benchmark functions. The proposed ABFO shows a marked improvement in performance over the original BFO and appears to be comparable with the PSO and GA.

  1. A formal model of interpersonal inference

    Directory of Open Access Journals (Sweden)

    Michael eMoutoussis

    2014-03-01

    Full Text Available Introduction: We propose that active Bayesian inference – a general framework for decision-making – can equally be applied to interpersonal exchanges. Social cognition, however, entails special challenges. We address these challenges through a novel formulation of a formal model and demonstrate its psychological significance. Method: We review relevant literature, especially with regards to interpersonal representations, formulate a mathematical model and present a simulation study. The model accommodates normative models from utility theory and places them within the broader setting of Bayesian inference. Crucially, we endow people's prior beliefs, into which utilities are absorbed, with preferences of self and others. The simulation illustrates the model's dynamics and furnishes elementary predictions of the theory. Results: 1. Because beliefs about self and others inform both the desirability and plausibility of outcomes, in this framework interpersonal representations become beliefs that have to be actively inferred. This inference, akin to 'mentalising' in the psychological literature, is based upon the outcomes of interpersonal exchanges. 2. We show how some well-known social-psychological phenomena (e.g. self-serving biases can be explained in terms of active interpersonal inference. 3. Mentalising naturally entails Bayesian updating of how people value social outcomes. Crucially this includes inference about one’s own qualities and preferences. Conclusion: We inaugurate a Bayes optimal framework for modelling intersubject variability in mentalising during interpersonal exchanges. Here, interpersonal representations are endowed with explicit functional and affective properties. We suggest the active inference framework lends itself to the study of psychiatric conditions where mentalising is distorted.

  2. Spatial filtring and thermocouple spatial filter

    International Nuclear Information System (INIS)

    Han Bing; Tong Yunxian

    1989-12-01

    The design and study on thermocouple spatial filter have been conducted for the flow measurement of integrated reactor coolant. The fundamental principle of spatial filtring, mathematical descriptions and analyses of thermocouple spatial filter are given

  3. Multiple Illuminant Colour Estimation via Statistical Inference on Factor Graphs.

    Science.gov (United States)

    Mutimbu, Lawrence; Robles-Kelly, Antonio

    2016-08-31

    This paper presents a method to recover a spatially varying illuminant colour estimate from scenes lit by multiple light sources. Starting with the image formation process, we formulate the illuminant recovery problem in a statistically datadriven setting. To do this, we use a factor graph defined across the scale space of the input image. In the graph, we utilise a set of illuminant prototypes computed using a data driven approach. As a result, our method delivers a pixelwise illuminant colour estimate being devoid of libraries or user input. The use of a factor graph also allows for the illuminant estimates to be recovered making use of a maximum a posteriori (MAP) inference process. Moreover, we compute the probability marginals by performing a Delaunay triangulation on our factor graph. We illustrate the utility of our method for pixelwise illuminant colour recovery on widely available datasets and compare against a number of alternatives. We also show sample colour correction results on real-world images.

  4. Estimating uncertainty of inference for validation

    Energy Technology Data Exchange (ETDEWEB)

    Booker, Jane M [Los Alamos National Laboratory; Langenbrunner, James R [Los Alamos National Laboratory; Hemez, Francois M [Los Alamos National Laboratory; Ross, Timothy J [UNM

    2010-09-30

    We present a validation process based upon the concept that validation is an inference-making activity. This has always been true, but the association has not been as important before as it is now. Previously, theory had been confirmed by more data, and predictions were possible based on data. The process today is to infer from theory to code and from code to prediction, making the role of prediction somewhat automatic, and a machine function. Validation is defined as determining the degree to which a model and code is an accurate representation of experimental test data. Imbedded in validation is the intention to use the computer code to predict. To predict is to accept the conclusion that an observable final state will manifest; therefore, prediction is an inference whose goodness relies on the validity of the code. Quantifying the uncertainty of a prediction amounts to quantifying the uncertainty of validation, and this involves the characterization of uncertainties inherent in theory/models/codes and the corresponding data. An introduction to inference making and its associated uncertainty is provided as a foundation for the validation problem. A mathematical construction for estimating the uncertainty in the validation inference is then presented, including a possibility distribution constructed to represent the inference uncertainty for validation under uncertainty. The estimation of inference uncertainty for validation is illustrated using data and calculations from Inertial Confinement Fusion (ICF). The ICF measurements of neutron yield and ion temperature were obtained for direct-drive inertial fusion capsules at the Omega laser facility. The glass capsules, containing the fusion gas, were systematically selected with the intent of establishing a reproducible baseline of high-yield 10{sup 13}-10{sup 14} neutron output. The deuterium-tritium ratio in these experiments was varied to study its influence upon yield. This paper on validation inference is the

  5. Deep Learning for Population Genetic Inference.

    Science.gov (United States)

    Sheehan, Sara; Song, Yun S

    2016-03-01

    Given genomic variation data from multiple individuals, computing the likelihood of complex population genetic models is often infeasible. To circumvent this problem, we introduce a novel likelihood-free inference framework by applying deep learning, a powerful modern technique in machine learning. Deep learning makes use of multilayer neural networks to learn a feature-based function from the input (e.g., hundreds of correlated summary statistics of data) to the output (e.g., population genetic parameters of interest). We demonstrate that deep learning can be effectively employed for population genetic inference and learning informative features of data. As a concrete application, we focus on the challenging problem of jointly inferring natural selection and demography (in the form of a population size change history). Our method is able to separate the global nature of demography from the local nature of selection, without sequential steps for these two factors. Studying demography and selection jointly is motivated by Drosophila, where pervasive selection confounds demographic analysis. We apply our method to 197 African Drosophila melanogaster genomes from Zambia to infer both their overall demography, and regions of their genome under selection. We find many regions of the genome that have experienced hard sweeps, and fewer under selection on standing variation (soft sweep) or balancing selection. Interestingly, we find that soft sweeps and balancing selection occur more frequently closer to the centromere of each chromosome. In addition, our demographic inference suggests that previously estimated bottlenecks for African Drosophila melanogaster are too extreme.

  6. Deep Learning for Population Genetic Inference.

    Directory of Open Access Journals (Sweden)

    Sara Sheehan

    2016-03-01

    Full Text Available Given genomic variation data from multiple individuals, computing the likelihood of complex population genetic models is often infeasible. To circumvent this problem, we introduce a novel likelihood-free inference framework by applying deep learning, a powerful modern technique in machine learning. Deep learning makes use of multilayer neural networks to learn a feature-based function from the input (e.g., hundreds of correlated summary statistics of data to the output (e.g., population genetic parameters of interest. We demonstrate that deep learning can be effectively employed for population genetic inference and learning informative features of data. As a concrete application, we focus on the challenging problem of jointly inferring natural selection and demography (in the form of a population size change history. Our method is able to separate the global nature of demography from the local nature of selection, without sequential steps for these two factors. Studying demography and selection jointly is motivated by Drosophila, where pervasive selection confounds demographic analysis. We apply our method to 197 African Drosophila melanogaster genomes from Zambia to infer both their overall demography, and regions of their genome under selection. We find many regions of the genome that have experienced hard sweeps, and fewer under selection on standing variation (soft sweep or balancing selection. Interestingly, we find that soft sweeps and balancing selection occur more frequently closer to the centromere of each chromosome. In addition, our demographic inference suggests that previously estimated bottlenecks for African Drosophila melanogaster are too extreme.

  7. Deep Learning for Population Genetic Inference

    Science.gov (United States)

    Sheehan, Sara; Song, Yun S.

    2016-01-01

    Given genomic variation data from multiple individuals, computing the likelihood of complex population genetic models is often infeasible. To circumvent this problem, we introduce a novel likelihood-free inference framework by applying deep learning, a powerful modern technique in machine learning. Deep learning makes use of multilayer neural networks to learn a feature-based function from the input (e.g., hundreds of correlated summary statistics of data) to the output (e.g., population genetic parameters of interest). We demonstrate that deep learning can be effectively employed for population genetic inference and learning informative features of data. As a concrete application, we focus on the challenging problem of jointly inferring natural selection and demography (in the form of a population size change history). Our method is able to separate the global nature of demography from the local nature of selection, without sequential steps for these two factors. Studying demography and selection jointly is motivated by Drosophila, where pervasive selection confounds demographic analysis. We apply our method to 197 African Drosophila melanogaster genomes from Zambia to infer both their overall demography, and regions of their genome under selection. We find many regions of the genome that have experienced hard sweeps, and fewer under selection on standing variation (soft sweep) or balancing selection. Interestingly, we find that soft sweeps and balancing selection occur more frequently closer to the centromere of each chromosome. In addition, our demographic inference suggests that previously estimated bottlenecks for African Drosophila melanogaster are too extreme. PMID:27018908

  8. Inferring Phylogenetic Networks Using PhyloNet.

    Science.gov (United States)

    Wen, Dingqiao; Yu, Yun; Zhu, Jiafan; Nakhleh, Luay

    2018-07-01

    PhyloNet was released in 2008 as a software package for representing and analyzing phylogenetic networks. At the time of its release, the main functionalities in PhyloNet consisted of measures for comparing network topologies and a single heuristic for reconciling gene trees with a species tree. Since then, PhyloNet has grown significantly. The software package now includes a wide array of methods for inferring phylogenetic networks from data sets of unlinked loci while accounting for both reticulation (e.g., hybridization) and incomplete lineage sorting. In particular, PhyloNet now allows for maximum parsimony, maximum likelihood, and Bayesian inference of phylogenetic networks from gene tree estimates. Furthermore, Bayesian inference directly from sequence data (sequence alignments or biallelic markers) is implemented. Maximum parsimony is based on an extension of the "minimizing deep coalescences" criterion to phylogenetic networks, whereas maximum likelihood and Bayesian inference are based on the multispecies network coalescent. All methods allow for multiple individuals per species. As computing the likelihood of a phylogenetic network is computationally hard, PhyloNet allows for evaluation and inference of networks using a pseudolikelihood measure. PhyloNet summarizes the results of the various analyzes and generates phylogenetic networks in the extended Newick format that is readily viewable by existing visualization software.

  9. The Spatial Politics of Spatial Representation

    DEFF Research Database (Denmark)

    Olesen, Kristian; Richardson, Tim

    2011-01-01

    spatial planning in Denmark reveals how fuzzy spatial representations and relational spatial concepts are being used to depoliticise strategic spatial planning processes and to camouflage spatial politics. The paper concludes that, while relational geography might play an important role in building......This paper explores the interplay between the spatial politics of new governance landscapes and innovations in the use of spatial representations in planning. The central premise is that planning experiments with new relational approaches become enmeshed in spatial politics. The case of strategic...

  10. Spatial Statistical Data Fusion (SSDF)

    Science.gov (United States)

    Braverman, Amy J.; Nguyen, Hai M.; Cressie, Noel

    2013-01-01

    As remote sensing for scientific purposes has transitioned from an experimental technology to an operational one, the selection of instruments has become more coordinated, so that the scientific community can exploit complementary measurements. However, tech nological and scientific heterogeneity across devices means that the statistical characteristics of the data they collect are different. The challenge addressed here is how to combine heterogeneous remote sensing data sets in a way that yields optimal statistical estimates of the underlying geophysical field, and provides rigorous uncertainty measures for those estimates. Different remote sensing data sets may have different spatial resolutions, different measurement error biases and variances, and other disparate characteristics. A state-of-the-art spatial statistical model was used to relate the true, but not directly observed, geophysical field to noisy, spatial aggregates observed by remote sensing instruments. The spatial covariances of the true field and the covariances of the true field with the observations were modeled. The observations are spatial averages of the true field values, over pixels, with different measurement noise superimposed. A kriging framework is used to infer optimal (minimum mean squared error and unbiased) estimates of the true field at point locations from pixel-level, noisy observations. A key feature of the spatial statistical model is the spatial mixed effects model that underlies it. The approach models the spatial covariance function of the underlying field using linear combinations of basis functions of fixed size. Approaches based on kriging require the inversion of very large spatial covariance matrices, and this is usually done by making simplifying assumptions about spatial covariance structure that simply do not hold for geophysical variables. In contrast, this method does not require these assumptions, and is also computationally much faster. This method is

  11. A Multi-Resolution Spatial Model for Large Datasets Based on the Skew-t Distribution

    KAUST Repository

    Tagle, Felipe; Castruccio, Stefano; Genton, Marc G.

    2017-01-01

    recently begun to appear in the spatial statistics literature, without much consideration, however, for the ability to capture dependence at multiple resolutions, and simultaneously achieve feasible inference for increasingly large data sets. This article

  12. Integrating resource selection information with spatial capture--recapture

    Science.gov (United States)

    Royle, J. Andrew; Chandler, Richard B.; Sun, Catherine C.; Fuller, Angela K.

    2013-01-01

    1. Understanding space usage and resource selection is a primary focus of many studies of animal populations. Usually, such studies are based on location data obtained from telemetry, and resource selection functions (RSFs) are used for inference. Another important focus of wildlife research is estimation and modeling population size and density. Recently developed spatial capture–recapture (SCR) models accomplish this objective using individual encounter history data with auxiliary spatial information on location of capture. SCR models include encounter probability functions that are intuitively related to RSFs, but to date, no one has extended SCR models to allow for explicit inference about space usage and resource selection.

  13. Goal inferences about robot behavior : goal inferences and human response behaviors

    NARCIS (Netherlands)

    Broers, H.A.T.; Ham, J.R.C.; Broeders, R.; De Silva, P.; Okada, M.

    2014-01-01

    This explorative research focused on the goal inferences human observers draw based on a robot's behavior, and the extent to which those inferences predict people's behavior in response to that robot. Results show that different robot behaviors cause different response behavior from people.

  14. Using Alien Coins to Test Whether Simple Inference Is Bayesian

    Science.gov (United States)

    Cassey, Peter; Hawkins, Guy E.; Donkin, Chris; Brown, Scott D.

    2016-01-01

    Reasoning and inference are well-studied aspects of basic cognition that have been explained as statistically optimal Bayesian inference. Using a simplified experimental design, we conducted quantitative comparisons between Bayesian inference and human inference at the level of individuals. In 3 experiments, with more than 13,000 participants, we…

  15. Lake Bacterial Assemblage Composition Is Sensitive to Biological Disturbance Caused by an Invasive Filter Feeder.

    Science.gov (United States)

    Denef, Vincent J; Carrick, Hunter J; Cavaletto, Joann; Chiang, Edna; Johengen, Thomas H; Vanderploeg, Henry A

    2017-01-01

    One approach to improve forecasts of how global change will affect ecosystem processes is to better understand how anthropogenic disturbances alter bacterial assemblages that drive biogeochemical cycles. Species invasions are important contributors to global change, but their impacts on bacterial community ecology are rarely investigated. Here, we studied direct impacts of invasive dreissenid mussels (IDMs), one of many invasive filter feeders, on freshwater lake bacterioplankton. We demonstrated that direct effects of IDMs reduced bacterial abundance and altered assemblage composition by preferentially removing larger and particle-associated bacteria. While this increased the relative abundances of many free-living bacterial taxa, some were susceptible to filter feeding, in line with efficient removal of phytoplankton cells of <2 μm. This selective removal of particle-associated and larger bacteria by IDMs altered inferred bacterial functional group representation, defined by carbon and energy source utilization. Specifically, we inferred an increased relative abundance of chemoorganoheterotrophs predicted to be capable of rhodopsin-dependent energy generation. In contrast to the few previous studies that have focused on the longer-term combined direct and indirect effects of IDMs on bacterioplankton, our study showed that IDMs act directly as a biological disturbance to which freshwater bacterial assemblages are sensitive. The negative impacts on particle-associated bacteria, which have been shown to be more active than free-living bacteria, and the inferred shifts in functional group representation raise the possibility that IDMs may directly alter bacterially mediated ecosystem functions. IMPORTANCE Freshwater bacteria play fundamental roles in global elemental cycling and are an intrinsic part of local food webs. Human activities are altering freshwater environments, and much has been learned regarding the sensitivity of bacterial assemblages to a variety of

  16. Bacterial growth laws reflect the evolutionary importance of energy efficiency.

    Science.gov (United States)

    Maitra, Arijit; Dill, Ken A

    2015-01-13

    We are interested in the balance of energy and protein synthesis in bacterial growth. How has evolution optimized this balance? We describe an analytical model that leverages extensive literature data on growth laws to infer the underlying fitness landscape and to draw inferences about what evolution has optimized in Escherichia coli. Is E. coli optimized for growth speed, energy efficiency, or some other property? Experimental data show that at its replication speed limit, E. coli produces about four mass equivalents of nonribosomal proteins for every mass equivalent of ribosomes. This ratio can be explained if the cell's fitness function is the the energy efficiency of cells under fast growth conditions, indicating a tradeoff between the high energy costs of ribosomes under fast growth and the high energy costs of turning over nonribosomal proteins under slow growth. This model gives insight into some of the complex nonlinear relationships between energy utilization and ribosomal and nonribosomal production as a function of cell growth conditions.

  17. Explanatory Preferences Shape Learning and Inference.

    Science.gov (United States)

    Lombrozo, Tania

    2016-10-01

    Explanations play an important role in learning and inference. People often learn by seeking explanations, and they assess the viability of hypotheses by considering how well they explain the data. An emerging body of work reveals that both children and adults have strong and systematic intuitions about what constitutes a good explanation, and that these explanatory preferences have a systematic impact on explanation-based processes. In particular, people favor explanations that are simple and broad, with the consequence that engaging in explanation can shape learning and inference by leading people to seek patterns and favor hypotheses that support broad and simple explanations. Given the prevalence of explanation in everyday cognition, understanding explanation is therefore crucial to understanding learning and inference. Copyright © 2016 Elsevier Ltd. All rights reserved.

  18. Fuzzy logic controller using different inference methods

    International Nuclear Information System (INIS)

    Liu, Z.; De Keyser, R.

    1994-01-01

    In this paper the design of fuzzy controllers by using different inference methods is introduced. Configuration of the fuzzy controllers includes a general rule-base which is a collection of fuzzy PI or PD rules, the triangular fuzzy data model and a centre of gravity defuzzification algorithm. The generalized modus ponens (GMP) is used with the minimum operator of the triangular norm. Under the sup-min inference rule, six fuzzy implication operators are employed to calculate the fuzzy look-up tables for each rule base. The performance is tested in simulated systems with MATLAB/SIMULINK. Results show the effects of using the fuzzy controllers with different inference methods and applied to different test processes

  19. Uncertainty in prediction and in inference

    International Nuclear Information System (INIS)

    Hilgevoord, J.; Uffink, J.

    1991-01-01

    The concepts of uncertainty in prediction and inference are introduced and illustrated using the diffraction of light as an example. The close relationship between the concepts of uncertainty in inference and resolving power is noted. A general quantitative measure of uncertainty in inference can be obtained by means of the so-called statistical distance between probability distributions. When applied to quantum mechanics, this distance leads to a measure of the distinguishability of quantum states, which essentially is the absolute value of the matrix element between the states. The importance of this result to the quantum mechanical uncertainty principle is noted. The second part of the paper provides a derivation of the statistical distance on the basis of the so-called method of support

  20. A Learning Algorithm for Multimodal Grammar Inference.

    Science.gov (United States)

    D'Ulizia, A; Ferri, F; Grifoni, P

    2011-12-01

    The high costs of development and maintenance of multimodal grammars in integrating and understanding input in multimodal interfaces lead to the investigation of novel algorithmic solutions in automating grammar generation and in updating processes. Many algorithms for context-free grammar inference have been developed in the natural language processing literature. An extension of these algorithms toward the inference of multimodal grammars is necessary for multimodal input processing. In this paper, we propose a novel grammar inference mechanism that allows us to learn a multimodal grammar from its positive samples of multimodal sentences. The algorithm first generates the multimodal grammar that is able to parse the positive samples of sentences and, afterward, makes use of two learning operators and the minimum description length metrics in improving the grammar description and in avoiding the over-generalization problem. The experimental results highlight the acceptable performances of the algorithm proposed in this paper since it has a very high probability of parsing valid sentences.

  1. Examples in parametric inference with R

    CERN Document Server

    Dixit, Ulhas Jayram

    2016-01-01

    This book discusses examples in parametric inference with R. Combining basic theory with modern approaches, it presents the latest developments and trends in statistical inference for students who do not have an advanced mathematical and statistical background. The topics discussed in the book are fundamental and common to many fields of statistical inference and thus serve as a point of departure for in-depth study. The book is divided into eight chapters: Chapter 1 provides an overview of topics on sufficiency and completeness, while Chapter 2 briefly discusses unbiased estimation. Chapter 3 focuses on the study of moments and maximum likelihood estimators, and Chapter 4 presents bounds for the variance. In Chapter 5, topics on consistent estimator are discussed. Chapter 6 discusses Bayes, while Chapter 7 studies some more powerful tests. Lastly, Chapter 8 examines unbiased and other tests. Senior undergraduate and graduate students in statistics and mathematics, and those who have taken an introductory cou...

  2. Grammatical inference algorithms, routines and applications

    CERN Document Server

    Wieczorek, Wojciech

    2017-01-01

    This book focuses on grammatical inference, presenting classic and modern methods of grammatical inference from the perspective of practitioners. To do so, it employs the Python programming language to present all of the methods discussed. Grammatical inference is a field that lies at the intersection of multiple disciplines, with contributions from computational linguistics, pattern recognition, machine learning, computational biology, formal learning theory and many others. Though the book is largely practical, it also includes elements of learning theory, combinatorics on words, the theory of automata and formal languages, plus references to real-world problems. The listings presented here can be directly copied and pasted into other programs, thus making the book a valuable source of ready recipes for students, academic researchers, and programmers alike, as well as an inspiration for their further development.>.

  3. Statistical inference based on divergence measures

    CERN Document Server

    Pardo, Leandro

    2005-01-01

    The idea of using functionals of Information Theory, such as entropies or divergences, in statistical inference is not new. However, in spite of the fact that divergence statistics have become a very good alternative to the classical likelihood ratio test and the Pearson-type statistic in discrete models, many statisticians remain unaware of this powerful approach.Statistical Inference Based on Divergence Measures explores classical problems of statistical inference, such as estimation and hypothesis testing, on the basis of measures of entropy and divergence. The first two chapters form an overview, from a statistical perspective, of the most important measures of entropy and divergence and study their properties. The author then examines the statistical analysis of discrete multivariate data with emphasis is on problems in contingency tables and loglinear models using phi-divergence test statistics as well as minimum phi-divergence estimators. The final chapter looks at testing in general populations, prese...

  4. High-order Composite Likelihood Inference for Max-Stable Distributions and Processes

    KAUST Repository

    Castruccio, Stefano; Huser, Raphaë l; Genton, Marc G.

    2015-01-01

    In multivariate or spatial extremes, inference for max-stable processes observed at a large collection of locations is a very challenging problem in computational statistics, and current approaches typically rely on less expensive composite likelihoods constructed from small subsets of data. In this work, we explore the limits of modern state-of-the-art computational facilities to perform full likelihood inference and to efficiently evaluate high-order composite likelihoods. With extensive simulations, we assess the loss of information of composite likelihood estimators with respect to a full likelihood approach for some widely-used multivariate or spatial extreme models, we discuss how to choose composite likelihood truncation to improve the efficiency, and we also provide recommendations for practitioners. This article has supplementary material online.

  5. High-order Composite Likelihood Inference for Max-Stable Distributions and Processes

    KAUST Repository

    Castruccio, Stefano

    2015-09-29

    In multivariate or spatial extremes, inference for max-stable processes observed at a large collection of locations is a very challenging problem in computational statistics, and current approaches typically rely on less expensive composite likelihoods constructed from small subsets of data. In this work, we explore the limits of modern state-of-the-art computational facilities to perform full likelihood inference and to efficiently evaluate high-order composite likelihoods. With extensive simulations, we assess the loss of information of composite likelihood estimators with respect to a full likelihood approach for some widely-used multivariate or spatial extreme models, we discuss how to choose composite likelihood truncation to improve the efficiency, and we also provide recommendations for practitioners. This article has supplementary material online.

  6. Supplementary Material for: High-Order Composite Likelihood Inference for Max-Stable Distributions and Processes

    KAUST Repository

    Castruccio, Stefano; Huser, Raphaë l; Genton, Marc G.

    2016-01-01

    In multivariate or spatial extremes, inference for max-stable processes observed at a large collection of points is a very challenging problem and current approaches typically rely on less expensive composite likelihoods constructed from small subsets of data. In this work, we explore the limits of modern state-of-the-art computational facilities to perform full likelihood inference and to efficiently evaluate high-order composite likelihoods. With extensive simulations, we assess the loss of information of composite likelihood estimators with respect to a full likelihood approach for some widely used multivariate or spatial extreme models, we discuss how to choose composite likelihood truncation to improve the efficiency, and we also provide recommendations for practitioners. This article has supplementary material online.

  7. Radiology of bacterial pneumonia

    International Nuclear Information System (INIS)

    Vilar, Jose; Domingo, Maria Luisa; Soto, Cristina; Cogollos, Jonathan

    2004-01-01

    Bacterial pneumonia is commonly encountered in clinical practice. Radiology plays a prominent role in the evaluation of pneumonia. Chest radiography is the most commonly used imaging tool in pneumonias due to its availability and excellent cost benefit ratio. CT should be used in unresolved cases or when complications of pneumonia are suspected. The main applications of radiology in pneumonia are oriented to detection, characterisation and follow-up, especially regarding complications. The classical classification of pneumonias into lobar and bronchial pneumonia has been abandoned for a more clinical classification. Thus, bacterial pneumonias are typified into three main groups: Community acquired pneumonia (CAD), Aspiration pneumonia and Nosocomial pneumonia (NP).The usual pattern of CAD is that of the previously called lobar pneumonia; an air-space consolidation limited to one lobe or segment. Nevertheless, the radiographic patterns of CAD may be variable and are often related to the causative agent. Aspiration pneumonia generally involves the lower lobes with bilateral multicentric opacities. Nosocomial Pneumonia (NP) occurs in hospitalised patients. The importance of NP is related to its high mortality and, thus, the need to obtain a prompt diagnosis. The role of imaging in NP is limited but decisive. The most valuable information is when the chest radiographs are negative and rule out pneumonia. The radiographic patterns of NP are very variable, most commonly showing diffuse multifocal involvement and pleural effusion. Imaging plays also an important role in the detection and evaluation of complications of bacterial pneumonias. In many of these cases, especially in hospitalised patients, chest CT must be obtained in order to better depict these associate findings

  8. Radiology of bacterial pneumonia

    Energy Technology Data Exchange (ETDEWEB)

    Vilar, Jose E-mail: vilar_jlu@gva.es; Domingo, Maria Luisa; Soto, Cristina; Cogollos, Jonathan

    2004-08-01

    Bacterial pneumonia is commonly encountered in clinical practice. Radiology plays a prominent role in the evaluation of pneumonia. Chest radiography is the most commonly used imaging tool in pneumonias due to its availability and excellent cost benefit ratio. CT should be used in unresolved cases or when complications of pneumonia are suspected. The main applications of radiology in pneumonia are oriented to detection, characterisation and follow-up, especially regarding complications. The classical classification of pneumonias into lobar and bronchial pneumonia has been abandoned for a more clinical classification. Thus, bacterial pneumonias are typified into three main groups: Community acquired pneumonia (CAD), Aspiration pneumonia and Nosocomial pneumonia (NP).The usual pattern of CAD is that of the previously called lobar pneumonia; an air-space consolidation limited to one lobe or segment. Nevertheless, the radiographic patterns of CAD may be variable and are often related to the causative agent. Aspiration pneumonia generally involves the lower lobes with bilateral multicentric opacities. Nosocomial Pneumonia (NP) occurs in hospitalised patients. The importance of NP is related to its high mortality and, thus, the need to obtain a prompt diagnosis. The role of imaging in NP is limited but decisive. The most valuable information is when the chest radiographs are negative and rule out pneumonia. The radiographic patterns of NP are very variable, most commonly showing diffuse multifocal involvement and pleural effusion. Imaging plays also an important role in the detection and evaluation of complications of bacterial pneumonias. In many of these cases, especially in hospitalised patients, chest CT must be obtained in order to better depict these associate findings.

  9. When does inferring reputation probability countervail temptation in cooperative behaviors for the prisoners’ dilemma game?

    International Nuclear Information System (INIS)

    Dai, Yu; Lu, Peng

    2015-01-01

    In evolutionary games, the temptation mechanism reduces cooperation percentage while the reputation mechanism promotes it. Inferring reputation theory proposes that agent's imitating neighbors with the highest reputation takes place with a probability. Although reputation promotes cooperation, when and how it enhances cooperation is still a question. This paper investigates the condition where the inferring reputation probability promotes cooperation. Hence, the effects of reputation and temptation on cooperation are explored under the spatial prisoners’ dilemma game, utilizing the methods of simulation and statistical analysis. Results show that temptation reduces cooperation unconditionally while reputation promotes it conditionally, i.e. reputation countervails temptation conditionally. When the inferring reputation probability is less than 0.5, reputation promotes cooperation substantially and thus countervails temptation. However, when the inferring reputation probability is larger than 0.5, its contribution to cooperation is relatively weak and cannot prevent temptation from undermining cooperation. Reputation even decreases cooperation together with temptation when the probability is higher than 0.8. It should be noticed that inferring reputation does not always succeed to countervail temptation and there is a specific interval for it to promote cooperation

  10. Improved Inference of Heteroscedastic Fixed Effects Models

    Directory of Open Access Journals (Sweden)

    Afshan Saeed

    2016-12-01

    Full Text Available Heteroscedasticity is a stern problem that distorts estimation and testing of panel data model (PDM. Arellano (1987 proposed the White (1980 estimator for PDM with heteroscedastic errors but it provides erroneous inference for the data sets including high leverage points. In this paper, our attempt is to improve heteroscedastic consistent covariance matrix estimator (HCCME for panel dataset with high leverage points. To draw robust inference for the PDM, our focus is to improve kernel bootstrap estimators, proposed by Racine and MacKinnon (2007. The Monte Carlo scheme is used for assertion of the results.

  11. Likelihood inference for unions of interacting discs

    DEFF Research Database (Denmark)

    Møller, Jesper; Helisova, K.

    2010-01-01

    This is probably the first paper which discusses likelihood inference for a random set using a germ-grain model, where the individual grains are unobservable, edge effects occur and other complications appear. We consider the case where the grains form a disc process modelled by a marked point...... process, where the germs are the centres and the marks are the associated radii of the discs. We propose to use a recent parametric class of interacting disc process models, where the minimal sufficient statistic depends on various geometric properties of the random set, and the density is specified......-based maximum likelihood inference and the effect of specifying different reference Poisson models....

  12. IMAGINE: Interstellar MAGnetic field INference Engine

    Science.gov (United States)

    Steininger, Theo

    2018-03-01

    IMAGINE (Interstellar MAGnetic field INference Engine) performs inference on generic parametric models of the Galaxy. The modular open source framework uses highly optimized tools and technology such as the MultiNest sampler (ascl:1109.006) and the information field theory framework NIFTy (ascl:1302.013) to create an instance of the Milky Way based on a set of parameters for physical observables, using Bayesian statistics to judge the mismatch between measured data and model prediction. The flexibility of the IMAGINE framework allows for simple refitting for newly available data sets and makes state-of-the-art Bayesian methods easily accessible particularly for random components of the Galactic magnetic field.

  13. Inferring causality from noisy time series data

    DEFF Research Database (Denmark)

    Mønster, Dan; Fusaroli, Riccardo; Tylén, Kristian

    2016-01-01

    Convergent Cross-Mapping (CCM) has shown high potential to perform causal inference in the absence of models. We assess the strengths and weaknesses of the method by varying coupling strength and noise levels in coupled logistic maps. We find that CCM fails to infer accurate coupling strength...... and even causality direction in synchronized time-series and in the presence of intermediate coupling. We find that the presence of noise deterministically reduces the level of cross-mapping fidelity, while the convergence rate exhibits higher levels of robustness. Finally, we propose that controlled noise...

  14. Spatial variability of microbial richness and diversity and relationships with soil organic carbon, texture and structure across an agricultural field

    DEFF Research Database (Denmark)

    Naveed, Muhammad; Herath, Lasantha; Møldrup, Per

    2016-01-01

    Highlights •Bacterial richness and Shannon diversity showed strong spatial autocorrelations. •Fungal richness and Shannon diversity did not show any clear spatial autocorrelations. •Ratio of clay to organic carbon was found a best predictor of bacterial richness and diversities. •Soil water...

  15. Model averaging, optimal inference and habit formation

    Directory of Open Access Journals (Sweden)

    Thomas H B FitzGerald

    2014-06-01

    Full Text Available Postulating that the brain performs approximate Bayesian inference generates principled and empirically testable models of neuronal function – the subject of much current interest in neuroscience and related disciplines. Current formulations address inference and learning under some assumed and particular model. In reality, organisms are often faced with an additional challenge – that of determining which model or models of their environment are the best for guiding behaviour. Bayesian model averaging – which says that an agent should weight the predictions of different models according to their evidence – provides a principled way to solve this problem. Importantly, because model evidence is determined by both the accuracy and complexity of the model, optimal inference requires that these be traded off against one another. This means an agent’s behaviour should show an equivalent balance. We hypothesise that Bayesian model averaging plays an important role in cognition, given that it is both optimal and realisable within a plausible neuronal architecture. We outline model averaging and how it might be implemented, and then explore a number of implications for brain and behaviour. In particular, we propose that model averaging can explain a number of apparently suboptimal phenomena within the framework of approximate (bounded Bayesian inference, focussing particularly upon the relationship between goal-directed and habitual behaviour.

  16. Efficient Bayesian inference for ARFIMA processes

    Science.gov (United States)

    Graves, T.; Gramacy, R. B.; Franzke, C. L. E.; Watkins, N. W.

    2015-03-01

    Many geophysical quantities, like atmospheric temperature, water levels in rivers, and wind speeds, have shown evidence of long-range dependence (LRD). LRD means that these quantities experience non-trivial temporal memory, which potentially enhances their predictability, but also hampers the detection of externally forced trends. Thus, it is important to reliably identify whether or not a system exhibits LRD. In this paper we present a modern and systematic approach to the inference of LRD. Rather than Mandelbrot's fractional Gaussian noise, we use the more flexible Autoregressive Fractional Integrated Moving Average (ARFIMA) model which is widely used in time series analysis, and of increasing interest in climate science. Unlike most previous work on the inference of LRD, which is frequentist in nature, we provide a systematic treatment of Bayesian inference. In particular, we provide a new approximate likelihood for efficient parameter inference, and show how nuisance parameters (e.g. short memory effects) can be integrated over in order to focus on long memory parameters, and hypothesis testing more directly. We illustrate our new methodology on the Nile water level data, with favorable comparison to the standard estimators.

  17. Campbell's and Rubin's Perspectives on Causal Inference

    Science.gov (United States)

    West, Stephen G.; Thoemmes, Felix

    2010-01-01

    Donald Campbell's approach to causal inference (D. T. Campbell, 1957; W. R. Shadish, T. D. Cook, & D. T. Campbell, 2002) is widely used in psychology and education, whereas Donald Rubin's causal model (P. W. Holland, 1986; D. B. Rubin, 1974, 2005) is widely used in economics, statistics, medicine, and public health. Campbell's approach focuses on…

  18. Bayesian structural inference for hidden processes

    Science.gov (United States)

    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.

  19. HIERARCHICAL PROBABILISTIC INFERENCE OF COSMIC SHEAR

    International Nuclear Information System (INIS)

    Schneider, Michael D.; Dawson, William A.; Hogg, David W.; Marshall, Philip J.; Bard, Deborah J.; Meyers, Joshua; Lang, Dustin

    2015-01-01

    Point estimators for the shearing of galaxy images induced by gravitational lensing involve a complex inverse problem in the presence of noise, pixelization, and model uncertainties. We present a probabilistic forward modeling approach to gravitational lensing inference that has the potential to mitigate the biased inferences in most common point estimators and is practical for upcoming lensing surveys. The first part of our statistical framework requires specification of a likelihood function for the pixel data in an imaging survey given parameterized models for the galaxies in the images. We derive the lensing shear posterior by marginalizing over all intrinsic galaxy properties that contribute to the pixel data (i.e., not limited to galaxy ellipticities) and learn the distributions for the intrinsic galaxy properties via hierarchical inference with a suitably flexible conditional probabilitiy distribution specification. We use importance sampling to separate the modeling of small imaging areas from the global shear inference, thereby rendering our algorithm computationally tractable for large surveys. With simple numerical examples we demonstrate the improvements in accuracy from our importance sampling approach, as well as the significance of the conditional distribution specification for the intrinsic galaxy properties when the data are generated from an unknown number of distinct galaxy populations with different morphological characteristics

  20. Interest, Inferences, and Learning from Texts

    Science.gov (United States)

    Clinton, Virginia; van den Broek, Paul

    2012-01-01

    Topic interest and learning from texts have been found to be positively associated with each other. However, the reason for this positive association is not well understood. The purpose of this study is to examine a cognitive process, inference generation, that could explain the positive association between interest and learning from texts. In…

  1. Ignorability in Statistical and Probabilistic Inference

    DEFF Research Database (Denmark)

    Jaeger, Manfred

    2005-01-01

    When dealing with incomplete data in statistical learning, or incomplete observations in probabilistic inference, one needs to distinguish the fact that a certain event is observed from the fact that the observed event has happened. Since the modeling and computational complexities entailed...

  2. Inverse Ising inference with correlated samples

    International Nuclear Information System (INIS)

    Obermayer, Benedikt; Levine, Erel

    2014-01-01

    Correlations between two variables of a high-dimensional system can be indicative of an underlying interaction, but can also result from indirect effects. Inverse Ising inference is a method to distinguish one from the other. Essentially, the parameters of the least constrained statistical model are learned from the observed correlations such that direct interactions can be separated from indirect correlations. Among many other applications, this approach has been helpful for protein structure prediction, because residues which interact in the 3D structure often show correlated substitutions in a multiple sequence alignment. In this context, samples used for inference are not independent but share an evolutionary history on a phylogenetic tree. Here, we discuss the effects of correlations between samples on global inference. Such correlations could arise due to phylogeny but also via other slow dynamical processes. We present a simple analytical model to address the resulting inference biases, and develop an exact method accounting for background correlations in alignment data by combining phylogenetic modeling with an adaptive cluster expansion algorithm. We find that popular reweighting schemes are only marginally effective at removing phylogenetic bias, suggest a rescaling strategy that yields better results, and provide evidence that our conclusions carry over to the frequently used mean-field approach to the inverse Ising problem. (paper)

  3. Evolutionary inference via the Poisson Indel Process.

    Science.gov (United States)

    Bouchard-Côté, Alexandre; Jordan, Michael I

    2013-01-22

    We address the problem of the joint statistical inference of phylogenetic trees and multiple sequence alignments from unaligned molecular sequences. This problem is generally formulated in terms of string-valued evolutionary processes along the branches of a phylogenetic tree. The classic evolutionary process, the TKF91 model [Thorne JL, Kishino H, Felsenstein J (1991) J Mol Evol 33(2):114-124] is a continuous-time Markov chain model composed of insertion, deletion, and substitution events. Unfortunately, this model gives rise to an intractable computational problem: The computation of the marginal likelihood under the TKF91 model is exponential in the number of taxa. In this work, we present a stochastic process, the Poisson Indel Process (PIP), in which the complexity of this computation is reduced to linear. The Poisson Indel Process is closely related to the TKF91 model, differing only in its treatment of insertions, but it has a global characterization as a Poisson process on the phylogeny. Standard results for Poisson processes allow key computations to be decoupled, which yields the favorable computational profile of inference under the PIP model. We present illustrative experiments in which Bayesian inference under the PIP model is compared with separate inference of phylogenies and alignments.

  4. Culture and Pragmatic Inference in Interpersonal Communication

    African Journals Online (AJOL)

    cognitive process, and that the human capacity for inference is crucially important ... been noted that research in interpersonal communication is currently pushing the ... communicative actions, the social-cultural world of everyday life is not only ... personal experiences of the authors', as documented over time and recreated ...

  5. Inference and the Introductory Statistics Course

    Science.gov (United States)

    Pfannkuch, Maxine; Regan, Matt; Wild, Chris; Budgett, Stephanie; Forbes, Sharleen; Harraway, John; Parsonage, Ross

    2011-01-01

    This article sets out some of the rationale and arguments for making major changes to the teaching and learning of statistical inference in introductory courses at our universities by changing from a norm-based, mathematical approach to more conceptually accessible computer-based approaches. The core problem of the inferential argument with its…

  6. Statistical Inference on the Canadian Middle Class

    Directory of Open Access Journals (Sweden)

    Russell Davidson

    2018-03-01

    Full Text Available Conventional wisdom says that the middle classes in many developed countries have recently suffered losses, in terms of both the share of the total population belonging to the middle class, and also their share in total income. Here, distribution-free methods are developed for inference on these shares, by means of deriving expressions for their asymptotic variances of sample estimates, and the covariance of the estimates. Asymptotic inference can be undertaken based on asymptotic normality. Bootstrap inference can be expected to be more reliable, and appropriate bootstrap procedures are proposed. As an illustration, samples of individual earnings drawn from Canadian census data are used to test various hypotheses about the middle-class shares, and confidence intervals for them are computed. It is found that, for the earlier censuses, sample sizes are large enough for asymptotic and bootstrap inference to be almost identical, but that, in the twenty-first century, the bootstrap fails on account of a strange phenomenon whereby many presumably different incomes in the data are rounded to one and the same value. Another difference between the centuries is the appearance of heavy right-hand tails in the income distributions of both men and women.

  7. Spurious correlations and inference in landscape genetics

    Science.gov (United States)

    Samuel A. Cushman; Erin L. Landguth

    2010-01-01

    Reliable interpretation of landscape genetic analyses depends on statistical methods that have high power to identify the correct process driving gene flow while rejecting incorrect alternative hypotheses. Little is known about statistical power and inference in individual-based landscape genetics. Our objective was to evaluate the power of causalmodelling with partial...

  8. Cortical information flow during inferences of agency

    NARCIS (Netherlands)

    Dogge, Myrthel; Hofman, Dennis; Boersma, Maria; Dijkerman, H Chris; Aarts, Henk

    2014-01-01

    Building on the recent finding that agency experiences do not merely rely on sensorimotor information but also on cognitive cues, this exploratory study uses electroencephalographic recordings to examine functional connectivity during agency inference processing in a setting where action and outcome

  9. Quasi-Experimental Designs for Causal Inference

    Science.gov (United States)

    Kim, Yongnam; Steiner, Peter

    2016-01-01

    When randomized experiments are infeasible, quasi-experimental designs can be exploited to evaluate causal treatment effects. The strongest quasi-experimental designs for causal inference are regression discontinuity designs, instrumental variable designs, matching and propensity score designs, and comparative interrupted time series designs. This…

  10. The importance of learning when making inferences

    Directory of Open Access Journals (Sweden)

    Jorg Rieskamp

    2008-03-01

    Full Text Available The assumption that people possess a repertoire of strategies to solve the inference problems they face has been made repeatedly. The experimental findings of two previous studies on strategy selection are reexamined from a learning perspective, which argues that people learn to select strategies for making probabilistic inferences. This learning process is modeled with the strategy selection learning (SSL theory, which assumes that people develop subjective expectancies for the strategies they have. They select strategies proportional to their expectancies, which are updated on the basis of experience. For the study by Newell, Weston, and Shanks (2003 it can be shown that people did not anticipate the success of a strategy from the beginning of the experiment. Instead, the behavior observed at the end of the experiment was the result of a learning process that can be described by the SSL theory. For the second study, by Br"oder and Schiffer (2006, the SSL theory is able to provide an explanation for why participants only slowly adapted to new environments in a dynamic inference situation. The reanalysis of the previous studies illustrates the importance of learning for probabilistic inferences.

  11. Colligation, Or the Logical Inference of Interconnection

    DEFF Research Database (Denmark)

    Falster, Peter

    1998-01-01

    laws or assumptions. Yet interconnection as an abstract concept seems to be without scientific underpinning in pure logic. Adopting a historical viewpoint, our aim is to show that the reasoning of interconnection may be identified with a neglected kind of logical inference, called "colligation...

  12. Colligation or, The Logical Inference of Interconnection

    DEFF Research Database (Denmark)

    Franksen, Ole Immanuel; Falster, Peter

    2000-01-01

    laws or assumptions. Yet interconnection as an abstract concept seems to be without scientific underpinning in oure logic. Adopting a historical viewpoint, our aim is to show that the reasoning of interconnection may be identified with a neglected kind of logical inference, called "colligation...

  13. Inferring motion and location using WLAN RSSI

    NARCIS (Netherlands)

    Kavitha Muthukrishnan, K.; van der Zwaag, B.J.; Havinga, Paul J.M.; Fuller, R.; Koutsoukos, X.

    2009-01-01

    We present novel algorithms to infer movement by making use of inherent fluctuations in the received signal strengths from existing WLAN infrastructure. We evaluate the performance of the presented algorithms based on classification metrics such as recall and precision using annotated traces

  14. Perceptual learning shapes multisensory causal inference via two distinct mechanisms.

    Science.gov (United States)

    McGovern, David P; Roudaia, Eugenie; Newell, Fiona N; Roach, Neil W

    2016-04-19

    To accurately represent the environment, our brains must integrate sensory signals from a common source while segregating those from independent sources. A reasonable strategy for performing this task is to restrict integration to cues that coincide in space and time. However, because multisensory signals are subject to differential transmission and processing delays, the brain must retain a degree of tolerance for temporal discrepancies. Recent research suggests that the width of this 'temporal binding window' can be reduced through perceptual learning, however, little is known about the mechanisms underlying these experience-dependent effects. Here, in separate experiments, we measure the temporal and spatial binding windows of human participants before and after training on an audiovisual temporal discrimination task. We show that training leads to two distinct effects on multisensory integration in the form of (i) a specific narrowing of the temporal binding window that does not transfer to spatial binding and (ii) a general reduction in the magnitude of crossmodal interactions across all spatiotemporal disparities. These effects arise naturally from a Bayesian model of causal inference in which learning improves the precision of audiovisual timing estimation, whilst concomitantly decreasing the prior expectation that stimuli emanate from a common source.

  15. Pathogenomic inference of virulence-associated genes in Leptospira interrogans.

    Directory of Open Access Journals (Sweden)

    Jason S Lehmann

    Full Text Available Leptospirosis is a globally important, neglected zoonotic infection caused by spirochetes of the genus Leptospira. Since genetic transformation remains technically limited for pathogenic Leptospira, a systems biology pathogenomic approach was used to infer leptospiral virulence genes by whole genome comparison of culture-attenuated Leptospira interrogans serovar Lai with its virulent, isogenic parent. Among the 11 pathogen-specific protein-coding genes in which non-synonymous mutations were found, a putative soluble adenylate cyclase with host cell cAMP-elevating activity, and two members of a previously unstudied ∼15 member paralogous gene family of unknown function were identified. This gene family was also uniquely found in the alpha-proteobacteria Bartonella bacilliformis and Bartonella australis that are geographically restricted to the Andes and Australia, respectively. How the pathogenic Leptospira and these two Bartonella species came to share this expanded gene family remains an evolutionary mystery. In vivo expression analyses demonstrated up-regulation of 10/11 Leptospira genes identified in the attenuation screen, and profound in vivo, tissue-specific up-regulation by members of the paralogous gene family, suggesting a direct role in virulence and host-pathogen interactions. The pathogenomic experimental design here is generalizable as a functional systems biology approach to studying bacterial pathogenesis and virulence and should encourage similar experimental studies of other pathogens.

  16. Pathogenomic inference of virulence-associated genes in Leptospira interrogans.

    Science.gov (United States)

    Lehmann, Jason S; Fouts, Derrick E; Haft, Daniel H; Cannella, Anthony P; Ricaldi, Jessica N; Brinkac, Lauren; Harkins, Derek; Durkin, Scott; Sanka, Ravi; Sutton, Granger; Moreno, Angelo; Vinetz, Joseph M; Matthias, Michael A

    2013-01-01

    Leptospirosis is a globally important, neglected zoonotic infection caused by spirochetes of the genus Leptospira. Since genetic transformation remains technically limited for pathogenic Leptospira, a systems biology pathogenomic approach was used to infer leptospiral virulence genes by whole genome comparison of culture-attenuated Leptospira interrogans serovar Lai with its virulent, isogenic parent. Among the 11 pathogen-specific protein-coding genes in which non-synonymous mutations were found, a putative soluble adenylate cyclase with host cell cAMP-elevating activity, and two members of a previously unstudied ∼15 member paralogous gene family of unknown function were identified. This gene family was also uniquely found in the alpha-proteobacteria Bartonella bacilliformis and Bartonella australis that are geographically restricted to the Andes and Australia, respectively. How the pathogenic Leptospira and these two Bartonella species came to share this expanded gene family remains an evolutionary mystery. In vivo expression analyses demonstrated up-regulation of 10/11 Leptospira genes identified in the attenuation screen, and profound in vivo, tissue-specific up-regulation by members of the paralogous gene family, suggesting a direct role in virulence and host-pathogen interactions. The pathogenomic experimental design here is generalizable as a functional systems biology approach to studying bacterial pathogenesis and virulence and should encourage similar experimental studies of other pathogens.

  17. Spatial reconstruction of single-cell gene expression data.

    Science.gov (United States)

    Satija, Rahul; Farrell, Jeffrey A; Gennert, David; Schier, Alexander F; Regev, Aviv

    2015-05-01

    Spatial localization is a key determinant of cellular fate and behavior, but methods for spatially resolved, transcriptome-wide gene expression profiling across complex tissues are lacking. RNA staining methods assay only a small number of transcripts, whereas single-cell RNA-seq, which measures global gene expression, separates cells from their native spatial context. Here we present Seurat, a computational strategy to infer cellular localization by integrating single-cell RNA-seq data with in situ RNA patterns. We applied Seurat to spatially map 851 single cells from dissociated zebrafish (Danio rerio) embryos and generated a transcriptome-wide map of spatial patterning. We confirmed Seurat's accuracy using several experimental approaches, then used the strategy to identify a set of archetypal expression patterns and spatial markers. Seurat correctly localizes rare subpopulations, accurately mapping both spatially restricted and scattered groups. Seurat will be applicable to mapping cellular localization within complex patterned tissues in diverse systems.

  18. Active inference, sensory attenuation and illusions.

    Science.gov (United States)

    Brown, Harriet; Adams, Rick A; Parees, Isabel; Edwards, Mark; Friston, Karl

    2013-11-01

    Active inference provides a simple and neurobiologically plausible account of how action and perception are coupled in producing (Bayes) optimal behaviour. This can be seen most easily as minimising prediction error: we can either change our predictions to explain sensory input through perception. Alternatively, we can actively change sensory input to fulfil our predictions. In active inference, this action is mediated by classical reflex arcs that minimise proprioceptive prediction error created by descending proprioceptive predictions. However, this creates a conflict between action and perception; in that, self-generated movements require predictions to override the sensory evidence that one is not actually moving. However, ignoring sensory evidence means that externally generated sensations will not be perceived. Conversely, attending to (proprioceptive and somatosensory) sensations enables the detection of externally generated events but precludes generation of actions. This conflict can be resolved by attenuating the precision of sensory evidence during movement or, equivalently, attending away from the consequences of self-made acts. We propose that this Bayes optimal withdrawal of precise sensory evidence during movement is the cause of psychophysical sensory attenuation. Furthermore, it explains the force-matching illusion and reproduces empirical results almost exactly. Finally, if attenuation is removed, the force-matching illusion disappears and false (delusional) inferences about agency emerge. This is important, given the negative correlation between sensory attenuation and delusional beliefs in normal subjects--and the reduction in the magnitude of the illusion in schizophrenia. Active inference therefore links the neuromodulatory optimisation of precision to sensory attenuation and illusory phenomena during the attribution of agency in normal subjects. It also provides a functional account of deficits in syndromes characterised by false inference

  19. Spatial filtering precedes motion detection.

    Science.gov (United States)

    Morgan, M J

    1992-01-23

    When we perceive motion on a television or cinema screen, there must be some process that allows us to track moving objects over time: if not, the result would be a conflicting mass of motion signals in all directions. A possible mechanism, suggested by studies of motion displacement in spatially random patterns, is that low-level motion detectors have a limited spatial range, which ensures that they tend to be stimulated over time by the same object. This model predicts that the direction of displacement of random patterns cannot be detected reliably above a critical absolute displacement value (Dmax) that is independent of the size or density of elements in the display. It has been inferred that Dmax is a measure of the size of motion detectors in the visual pathway. Other studies, however, have shown that Dmax increases with element size, in which case the most likely interpretation is that Dmax depends on the probability of false matches between pattern elements following a displacement. These conflicting accounts are reconciled here by showing that Dmax is indeed determined by the spacing between the elements in the pattern, but only after fine detail has been removed by a physiological prefiltering stage: the filter required to explain the data has a similar size to the receptive field of neurons in the primate magnocellular pathway. The model explains why Dmax can be increased by removing high spatial frequencies from random patterns, and simplifies our view of early motion detection.

  20. Laboratory-Cultured Strains of the Sea Anemone Exaiptasia Reveal Distinct Bacterial Communities

    KAUST Repository

    Herrera Sarrias, Marcela; Ziegler, Maren; Voolstra, Christian R.; Aranda, Manuel

    2017-01-01

    Exaiptasia is a laboratory sea anemone model system for stony corals. Two clonal strains are commonly used, referred to as H2 and CC7, that originate from two genetically distinct lineages and that differ in their Symbiodinium specificity. However, little is known about their other microbial associations. Here, we examined and compared the taxonomic composition of the bacterial assemblages of these two symbiotic Exaiptasia strains, both of which have been cultured in the laboratory long-term under identical conditions. We found distinct bacterial microbiota for each strain, indicating the presence of host-specific microbial consortia. Putative differences in the bacterial functional profiles (i.e., enrichment and depletion of various metabolic processes) based on taxonomic inference were also detected, further suggesting functional differences of the microbiomes associated with these lineages. Our study contributes to the current knowledge of the Exaiptasia holobiont by comparing the bacterial diversity of two commonly used strains as models for coral research.

  1. Laboratory-Cultured Strains of the Sea Anemone Exaiptasia Reveal Distinct Bacterial Communities

    KAUST Repository

    Herrera Sarrias, Marcela

    2017-05-02

    Exaiptasia is a laboratory sea anemone model system for stony corals. Two clonal strains are commonly used, referred to as H2 and CC7, that originate from two genetically distinct lineages and that differ in their Symbiodinium specificity. However, little is known about their other microbial associations. Here, we examined and compared the taxonomic composition of the bacterial assemblages of these two symbiotic Exaiptasia strains, both of which have been cultured in the laboratory long-term under identical conditions. We found distinct bacterial microbiota for each strain, indicating the presence of host-specific microbial consortia. Putative differences in the bacterial functional profiles (i.e., enrichment and depletion of various metabolic processes) based on taxonomic inference were also detected, further suggesting functional differences of the microbiomes associated with these lineages. Our study contributes to the current knowledge of the Exaiptasia holobiont by comparing the bacterial diversity of two commonly used strains as models for coral research.

  2. Bacterial polyhydroxyalkanoates: Still fabulous?

    Science.gov (United States)

    Możejko-Ciesielska, Justyna; Kiewisz, Robert

    2016-11-01

    Bacterial polyhydroxyalkanoates (PHA) are polyesters accumulated as carbon and energy storage materials under limited growth conditions in the presence of excess carbon sources. They have been developed as biomaterials with unique properties for the past many years being considered as a potential substitute for conventional non-degradable plastics. Due to the increasing concern towards global climate change, depleting petroleum resource and problems with an utilization of a growing number of synthetic plastics, PHAs have gained much more attention from industry and research. These environmentally friendly microbial polymers have great potential in biomedical, agricultural, and industrial applications. However, their production on a large scale is still limited. This paper describes the backgrounds of PHAs and discussed the current state of knowledge on the polyhydroxyalkanoates. Ability of bacteria to convert different carbon sources to PHAs, the opportunities and challenges of their introduction to global market as valuable renewable products have been also discussed. Copyright © 2016 Elsevier GmbH. All rights reserved.

  3. Energetics of bacterial adhesion

    International Nuclear Information System (INIS)

    Loosdrecht, M.C.M. van; Zehnder, A.J.B.

    1990-01-01

    For the description of bacterial adhesion phenomena two different physico-chemical approaches are available. The first one, based on a surface Gibbs energy balance, assumes intimate contact between the interacting surfaces. The second approach, based on colloid chemical theories (DLVO theory), allows for two types of adhesion: 1) secondary minimum adhesion, which is often weak and reversible, and 2) irreversible primary minimum adhesion. In the secondary minimum adhesion a thin water film remains present between the interacting surface. The merits of both approaches are discussed in this paper. In addition, the methods available to measure the physico-chemical surface characteristics of bacteria and the influence of adsorbing (in)organic compounds, extracellular polymers and cell surface appendages on adhesion are summarized. (author) 2 figs., 1 tab., 50 refs

  4. Biosensors of bacterial cells.

    Science.gov (United States)

    Burlage, Robert S; Tillmann, Joshua

    2017-07-01

    Biosensors are devices which utilize both an electrical component (transducer) and a biological component to study an environment. They are typically used to examine biological structures, organisms and processes. The field of biosensors has now become so large and varied that the technology can often seem impenetrable. Yet the principles which underlie the technology are uncomplicated, even if the details of the mechanisms are elusive. In this review we confine our analysis to relatively current advancements in biosensors for the detection of whole bacterial cells. This includes biosensors which rely on an added labeled component and biosensors which do not have a labeled component and instead detect the binding event or bound structure on the transducer. Methods to concentrate the bacteria prior to biosensor analysis are also described. The variety of biosensor types and their actual and potential uses are described. Copyright © 2016 Elsevier B.V. All rights reserved.

  5. Bacterial mitotic machineries

    DEFF Research Database (Denmark)

    Gerdes, Kenn; Møller-Jensen, Jakob; Ebersbach, Gitte

    2004-01-01

    Here, we review recent progress that yields fundamental new insight into the molecular mechanisms behind plasmid and chromosome segregation in prokaryotic cells. In particular, we describe how prokaryotic actin homologs form mitotic machineries that segregate DNA before cell division. Thus, the P......M protein of plasmid R1 forms F actin-like filaments that separate and move plasmid DNA from mid-cell to the cell poles. Evidence from three different laboratories indicate that the morphogenetic MreB protein may be involved in segregation of the bacterial chromosome.......Here, we review recent progress that yields fundamental new insight into the molecular mechanisms behind plasmid and chromosome segregation in prokaryotic cells. In particular, we describe how prokaryotic actin homologs form mitotic machineries that segregate DNA before cell division. Thus, the Par...

  6. Exploring bacterial lignin degradation.

    Science.gov (United States)

    Brown, Margaret E; Chang, Michelle C Y

    2014-04-01

    Plant biomass represents a renewable carbon feedstock that could potentially be used to replace a significant level of petroleum-derived chemicals. One major challenge in its utilization is that the majority of this carbon is trapped in the recalcitrant structural polymers of the plant cell wall. Deconstruction of lignin is a key step in the processing of biomass to useful monomers but remains challenging. Microbial systems can provide molecular information on lignin depolymerization as they have evolved to break lignin down using metalloenzyme-dependent radical pathways. Both fungi and bacteria have been observed to metabolize lignin; however, their differential reactivity with this substrate indicates that they may utilize different chemical strategies for its breakdown. This review will discuss recent advances in studying bacterial lignin degradation as an approach to exploring greater diversity in the environment. Copyright © 2013 Elsevier Ltd. All rights reserved.

  7. Studying bacterial multispecies biofilms

    DEFF Research Database (Denmark)

    Røder, Henriette Lyng; Sørensen, Søren Johannes; Burmølle, Mette

    2016-01-01

    The high prevalence and significance of multispecies biofilms have now been demonstrated in various bacterial habitats with medical, industrial, and ecological relevance. It is highly evident that several species of bacteria coexist and interact in biofilms, which highlights the need for evaluating...... the approaches used to study these complex communities. This review focuses on the establishment of multispecies biofilms in vitro, interspecies interactions in microhabitats, and how to select communities for evaluation. Studies have used different experimental approaches; here we evaluate the benefits...... and drawbacks of varying the degree of complexity. This review aims to facilitate multispecies biofilm research in order to expand the current limited knowledge on interspecies interactions. Recent technological advances have enabled total diversity analysis of highly complex and diverse microbial communities...

  8. Anaerobes in bacterial vaginosis

    Directory of Open Access Journals (Sweden)

    Aggarwal A

    2003-01-01

    Full Text Available Four hundred high vaginal swabs were taken from patients attending gynaecology and obstetrics department of Govt. medical college, Amritsar. The patients were divided into four groups i.e. women in pregnancy (Group I, in labour/post partum (Group II, with abnormal vaginal discharge or bacterial vaginosis (Group III and asymptomatic women as control (Group IV. Anaerobic culture of vaginal swabs revealed that out of 400 cases, 212(53% were culture positive. Maximum isolation of anaerobes was in group III (84% followed by group II (56%, group I (36% and control group (15%. Gram positive anaerobes (69.2% out numbered gram negatives (30.8%. Among various isolates Peptostreptococcus spp. and Bacteroides spp. were predominant.

  9. Bacterial subversion of host actin dynamics at the plasma membrane.

    Science.gov (United States)

    Carabeo, Rey

    2011-10-01

    Invasion of non-phagocytic cells by a number of bacterial pathogens involves the subversion of the actin cytoskeletal remodelling machinery to produce actin-rich cell surface projections designed to engulf the bacteria. The signalling that occurs to induce these actin-rich structures has considerable overlap among a diverse group of bacteria. The molecular organization within these structures act in concert to internalize the invading pathogen. This dynamic process could be subdivided into three acts - actin recruitment, engulfment, and finally, actin disassembly/internalization. This review will present the current state of knowledge of the molecular processes involved in each stage of bacterial invasion, and provide a perspective that highlights the temporal and spatial control of actin remodelling that occurs during bacterial invasion. © 2011 Blackwell Publishing Ltd.

  10. A Straightforward Approach for 3D Bacterial Printing.

    Science.gov (United States)

    Lehner, Benjamin A E; Schmieden, Dominik T; Meyer, Anne S

    2017-07-21

    Sustainable and personally tailored materials production is an emerging challenge to society. Living organisms can produce and pattern an extraordinarily wide range of different molecules in a sustainable way. These natural systems offer an abundant source of inspiration for the development of new environmentally friendly materials production techniques. In this paper, we describe the first steps toward the 3-dimensional printing of bacterial cultures for materials production and patterning. This methodology combines the capability of bacteria to form new materials with the reproducibility and tailored approach of 3D printing systems. For this purpose, a commercial 3D printer was modified for bacterial systems, and new alginate-based bioink chemistry was developed. Printing temperature, printhead speed, and bioink extrusion rate were all adapted and customized to maximize bacterial health and spatial resolution of printed structures. Our combination of 3D printing technology with biological systems enables a sustainable approach for the production of numerous new materials.

  11. Bacterial meningitis in immunocompromised patients

    NARCIS (Netherlands)

    van Veen, K.E.B.

    2018-01-01

    Bacterial meningitis is an acute infection of the meninges, in The Netherlands most commonly caused by Streptococcus pneumoniae and Neisseria meningitides. Risk factors for acquiring bacterial meningitis include a decreased function of the immune system. The aim of this thesis was to study

  12. Bacteriële meningitis

    NARCIS (Netherlands)

    Brouwer, M. C.; van de Beek, D.

    2012-01-01

    Bacterial meningitis is a severe disease which affects 35.000 Europeans each year and has a mortality rate of about 20%. During the past 25 years the epidemiology of bacterial meningitis has changed significantly due to the implementation of vaccination against Haemophilus influenzae, Neisseria

  13. Dynamics of genome rearrangement in bacterial populations.

    Directory of Open Access Journals (Sweden)

    Aaron E Darling

    2008-07-01

    Full Text Available Genome structure variation has profound impacts on phenotype in organisms ranging from microbes to humans, yet little is known about how natural selection acts on genome arrangement. Pathogenic bacteria such as Yersinia pestis, which causes bubonic and pneumonic plague, often exhibit a high degree of genomic rearrangement. The recent availability of several Yersinia genomes offers an unprecedented opportunity to study the evolution of genome structure and arrangement. We introduce a set of statistical methods to study patterns of rearrangement in circular chromosomes and apply them to the Yersinia. We constructed a multiple alignment of eight Yersinia genomes using Mauve software to identify 78 conserved segments that are internally free from genome rearrangement. Based on the alignment, we applied Bayesian statistical methods to infer the phylogenetic inversion history of Yersinia. The sampling of genome arrangement reconstructions contains seven parsimonious tree topologies, each having different histories of 79 inversions. Topologies with a greater number of inversions also exist, but were sampled less frequently. The inversion phylogenies agree with results suggested by SNP patterns. We then analyzed reconstructed inversion histories to identify patterns of rearrangement. We confirm an over-representation of "symmetric inversions"-inversions with endpoints that are equally distant from the origin of chromosomal replication. Ancestral genome arrangements demonstrate moderate preference for replichore balance in Yersinia. We found that all inversions are shorter than expected under a neutral model, whereas inversions acting within a single replichore are much shorter than expected. We also found evidence for a canonical configuration of the origin and terminus of replication. Finally, breakpoint reuse analysis reveals that inversions with endpoints proximal to the origin of DNA replication are nearly three times more frequent. Our findings

  14. Bacterial Protein-Tyrosine Kinases

    DEFF Research Database (Denmark)

    Shi, Lei; Kobir, Ahasanul; Jers, Carsten

    2010-01-01

    in exopolysaccharide production, virulence, DNA metabolism, stress response and other key functions of the bacterial cell. BY-kinases act through autophosphorylation (mainly in exopolysaccharide production) and phosphorylation of other proteins, which have in most cases been shown to be activated by tyrosine......Bacteria and Eukarya share essentially the same family of protein-serine/threonine kinases, also known as the Hanks-type kinases. However, when it comes to protein-tyrosine phosphorylation, bacteria seem to have gone their own way. Bacterial protein-tyrosine kinases (BY-kinases) are bacterial...... and highlighted their importance in bacterial physiology. Having no orthologues in Eukarya, BY-kinases are receiving a growing attention from the biomedical field, since they represent a particularly promising target for anti-bacterial drug design....

  15. Molecular approaches for bacterial azoreductases

    Directory of Open Access Journals (Sweden)

    Montira Leelakriangsak

    2013-12-01

    Full Text Available Azo dyes are the dominant types of synthetic dyes, widely used in textiles, foods, leather, printing, tattooing, cosmetics, and pharmaceutical industries. Many microorganisms are able to decolorize azo dyes, and there is increasing interest in biological waste treatment methods. Bacterial azoreductases can cleave azo linkages (-N=N- in azo dyes, forming aromatic amines. This review mainly focuses on employing molecular approaches, including gene manipulation and recombinant strains, to study bacterial azoreductases. The construction of the recombinant protein by cloning and the overexpression of azoreductase is described. The mechanisms and function of bacterial azoreductases can be studied by other molecular techniques discussed in this review, such as RT-PCR, southern blot analysis, western blot analysis, zymography, and muta-genesis in order to understand bacterial azoreductase properties, function and application. In addition, understanding the regulation of azoreductase gene expression will lead to the systematic use of gene manipulation in bacterial strains for new strategies in future waste remediation technologies.

  16. Supraglacial bacterial community structures vary across the Greenland ice sheet

    DEFF Research Database (Denmark)

    Cameron, Karen A.; Stibal, Marek; Zarsky, Jakub D.

    2016-01-01

    The composition and spatial variability of microbial communities that reside within the extensive (>200 000 km(2)) biologically active area encompassing the Greenland ice sheet (GrIS) is hypothesized to be variable. We examined bacterial communities from cryoconite debris and surface ice across...... the GrIS, using sequence analysis and quantitative PCR of 16S rRNA genes from co-extracted DNA and RNA. Communities were found to differ across the ice sheet, with 82.8% of the total calculated variation attributed to spatial distribution on a scale of tens of kilometers separation. Amplicons related...... to Sphingobacteriaceae, Pseudanabaenaceae and WPS-2 accounted for the greatest portion of calculated dissimilarities. The bacterial communities of ice and cryoconite were moderately similar (global R = 0.360, P = 0.002) and the sampled surface type (ice versus cryoconite) did not contribute heavily towards community...

  17. Bacterial Population Genetics in a Forensic Context

    Energy Technology Data Exchange (ETDEWEB)

    Velsko, S P

    2009-11-02

    This report addresses the recent Department of Homeland Security (DHS) call for a Phase I study to (1) assess gaps in the forensically relevant knowledge about the population genetics of eight bacterial agents of concern, (2) formulate a technical roadmap to address those gaps, and (3) identify new bioinformatics tools that would be necessary to analyze and interpret population genetic data in a forensic context. The eight organisms that were studied are B. anthracis, Y. pestis, F. tularensis, Brucella spp., E. coli O157/H7, Burkholderia mallei, Burkholderia pseudomallei, and C. botulinum. Our study focused on the use of bacterial population genetics by forensic investigators to test hypotheses about the possible provenance of an agent that was used in a crime or act of terrorism. Just as human population genetics underpins the calculations of match probabilities for human DNA evidence, bacterial population genetics determines the level of support that microbial DNA evidence provides for or against certain well-defined hypotheses about the origins of an infecting strain. Our key findings are: (1) Bacterial population genetics is critical for answering certain types of questions in a probabilistic manner, akin (but not identical) to 'match probabilities' in DNA forensics. (2) A basic theoretical framework for calculating likelihood ratios or posterior probabilities for forensic hypotheses based on microbial genetic comparisons has been formulated. This 'inference-on-networks' framework has deep but simple connections to the population genetics of mtDNA and Y-STRs in human DNA forensics. (3) The 'phylogeographic' approach to identifying microbial sources is not an adequate basis for understanding bacterial population genetics in a forensic context, and has limited utility, even for generating 'leads' with respect to strain origin. (4) A collection of genotyped isolates obtained opportunistically from international locations

  18. Handbook of Spatial Statistics

    CERN Document Server

    Gelfand, Alan E

    2010-01-01

    Offers an introduction detailing the evolution of the field of spatial statistics. This title focuses on the three main branches of spatial statistics: continuous spatial variation (point referenced data); discrete spatial variation, including lattice and areal unit data; and, spatial point patterns.

  19. Lunar true polar wander inferred from polar hydrogen.

    Science.gov (United States)

    Siegler, M A; Miller, R S; Keane, J T; Laneuville, M; Paige, D A; Matsuyama, I; Lawrence, D J; Crotts, A; Poston, M J

    2016-03-24

    The earliest dynamic and thermal history of the Moon is not well understood. The hydrogen content of deposits near the lunar poles may yield insight into this history, because these deposits (which are probably composed of water ice) survive only if they remain in permanent shadow. If the orientation of the Moon has changed, then the locations of the shadowed regions will also have changed. The polar hydrogen deposits have been mapped by orbiting neutron spectrometers, and their observed spatial distribution does not match the expected distribution of water ice inferred from present-day lunar temperatures. This finding is in contrast to the distribution of volatiles observed in similar thermal environments at Mercury's poles. Here we show that polar hydrogen preserves evidence that the spin axis of the Moon has shifted: the hydrogen deposits are antipodal and displaced equally from each pole along opposite longitudes. From the direction and magnitude of the inferred reorientation, and from analysis of the moments of inertia of the Moon, we hypothesize that this change in the spin axis, known as true polar wander, was caused by a low-density thermal anomaly beneath the Procellarum region. Radiogenic heating within this region resulted in the bulk of lunar mare volcanism and altered the density structure of the Moon, changing its moments of inertia. This resulted in true polar wander consistent with the observed remnant polar hydrogen. This thermal anomaly still exists and, in part, controls the current orientation of the Moon. The Procellarum region was most geologically active early in lunar history, which implies that polar wander initiated billions of years ago and that a large portion of the measured polar hydrogen is ancient, recording early delivery of water to the inner Solar System. Our hypothesis provides an explanation for the antipodal distribution of lunar polar hydrogen, and connects polar volatiles to the geologic and geophysical evolution of the Moon

  20. BayesCLUMPY: BAYESIAN INFERENCE WITH CLUMPY DUSTY TORUS MODELS

    International Nuclear Information System (INIS)

    Asensio Ramos, A.; Ramos Almeida, C.

    2009-01-01

    Our aim is to present a fast and general Bayesian inference framework based on the synergy between machine learning techniques and standard sampling methods and apply it to infer the physical properties of clumpy dusty torus using infrared photometric high spatial resolution observations of active galactic nuclei. We make use of the Metropolis-Hastings Markov Chain Monte Carlo algorithm for sampling the posterior distribution function. Such distribution results from combining all a priori knowledge about the parameters of the model and the information introduced by the observations. The main difficulty resides in the fact that the model used to explain the observations is computationally demanding and the sampling is very time consuming. For this reason, we apply a set of artificial neural networks that are used to approximate and interpolate a database of models. As a consequence, models not present in the original database can be computed ensuring continuity. We focus on the application of this solution scheme to the recently developed public database of clumpy dusty torus models. The machine learning scheme used in this paper allows us to generate any model from the database using only a factor of 10 -4 of the original size of the database and a factor of 10 -3 in computing time. The posterior distribution obtained for each model parameter allows us to investigate how the observations constrain the parameters and which ones remain partially or completely undetermined, providing statistically relevant confidence intervals. As an example, the application to the nuclear region of Centaurus A shows that the optical depth of the clouds, the total number of clouds, and the radial extent of the cloud distribution zone are well constrained using only six filters. The code is freely available from the authors.

  1. Likelihood inference for unions of interacting discs

    DEFF Research Database (Denmark)

    Møller, Jesper; Helisová, Katarina

    To the best of our knowledge, this is the first paper which discusses likelihood inference or a random set using a germ-grain model, where the individual grains are unobservable edge effects occur, and other complications appear. We consider the case where the grains form a disc process modelled...... is specified with respect to a given marked Poisson model (i.e. a Boolean model). We show how edge effects and other complications can be handled by considering a certain conditional likelihood. Our methodology is illustrated by analyzing Peter Diggle's heather dataset, where we discuss the results...... of simulation-based maximum likelihood inference and the effect of specifying different reference Poisson models....

  2. An Intuitive Dashboard for Bayesian Network Inference

    International Nuclear Information System (INIS)

    Reddy, Vikas; Farr, Anna Charisse; Wu, Paul; Mengersen, Kerrie; Yarlagadda, Prasad K D V

    2014-01-01

    Current Bayesian network software packages provide good graphical interface for users who design and develop Bayesian networks for various applications. However, the intended end-users of these networks may not necessarily find such an interface appealing and at times it could be overwhelming, particularly when the number of nodes in the network is large. To circumvent this problem, this paper presents an intuitive dashboard, which provides an additional layer of abstraction, enabling the end-users to easily perform inferences over the Bayesian networks. Unlike most software packages, which display the nodes and arcs of the network, the developed tool organises the nodes based on the cause-and-effect relationship, making the user-interaction more intuitive and friendly. In addition to performing various types of inferences, the users can conveniently use the tool to verify the behaviour of the developed Bayesian network. The tool has been developed using QT and SMILE libraries in C++

  3. Bayesianism and inference to the best explanation

    Directory of Open Access Journals (Sweden)

    Valeriano IRANZO

    2008-01-01

    Full Text Available Bayesianism and Inference to the best explanation (IBE are two different models of inference. Recently there has been some debate about the possibility of “bayesianizing” IBE. Firstly I explore several alternatives to include explanatory considerations in Bayes’s Theorem. Then I distinguish two different interpretations of prior probabilities: “IBE-Bayesianism” (IBE-Bay and “frequentist-Bayesianism” (Freq-Bay. After detailing the content of the latter, I propose a rule for assessing the priors. I also argue that Freq-Bay: (i endorses a role for explanatory value in the assessment of scientific hypotheses; (ii avoids a purely subjectivist reading of prior probabilities; and (iii fits better than IBE-Bayesianism with two basic facts about science, i.e., the prominent role played by empirical testing and the existence of many scientific theories in the past that failed to fulfil their promises and were subsequently abandoned.

  4. Dopamine, reward learning, and active inference

    Directory of Open Access Journals (Sweden)

    Thomas eFitzgerald

    2015-11-01

    Full Text Available Temporal difference learning models propose phasic dopamine signalling encodes reward prediction errors that drive learning. This is supported by studies where optogenetic stimulation of dopamine neurons can stand in lieu of actual reward. Nevertheless, a large body of data also shows that dopamine is not necessary for learning, and that dopamine depletion primarily affects task performance. We offer a resolution to this paradox based on an hypothesis that dopamine encodes the precision of beliefs about alternative actions, and thus controls the outcome-sensitivity of behaviour. We extend an active inference scheme for solving Markov decision processes to include learning, and show that simulated dopamine dynamics strongly resemble those actually observed during instrumental conditioning. Furthermore, simulated dopamine depletion impairs performance but spares learning, while simulated excitation of dopamine neurons drives reward learning, through aberrant inference about outcome states. Our formal approach provides a novel and parsimonious reconciliation of apparently divergent experimental findings.

  5. Dopamine, reward learning, and active inference.

    Science.gov (United States)

    FitzGerald, Thomas H B; Dolan, Raymond J; Friston, Karl

    2015-01-01

    Temporal difference learning models propose phasic dopamine signaling encodes reward prediction errors that drive learning. This is supported by studies where optogenetic stimulation of dopamine neurons can stand in lieu of actual reward. Nevertheless, a large body of data also shows that dopamine is not necessary for learning, and that dopamine depletion primarily affects task performance. We offer a resolution to this paradox based on an hypothesis that dopamine encodes the precision of beliefs about alternative actions, and thus controls the outcome-sensitivity of behavior. We extend an active inference scheme for solving Markov decision processes to include learning, and show that simulated dopamine dynamics strongly resemble those actually observed during instrumental conditioning. Furthermore, simulated dopamine depletion impairs performance but spares learning, while simulated excitation of dopamine neurons drives reward learning, through aberrant inference about outcome states. Our formal approach provides a novel and parsimonious reconciliation of apparently divergent experimental findings.

  6. Inferring genetic interactions from comparative fitness data.

    Science.gov (United States)

    Crona, Kristina; Gavryushkin, Alex; Greene, Devin; Beerenwinkel, Niko

    2017-12-20

    Darwinian fitness is a central concept in evolutionary biology. In practice, however, it is hardly possible to measure fitness for all genotypes in a natural population. Here, we present quantitative tools to make inferences about epistatic gene interactions when the fitness landscape is only incompletely determined due to imprecise measurements or missing observations. We demonstrate that genetic interactions can often be inferred from fitness rank orders, where all genotypes are ordered according to fitness, and even from partial fitness orders. We provide a complete characterization of rank orders that imply higher order epistasis. Our theory applies to all common types of gene interactions and facilitates comprehensive investigations of diverse genetic interactions. We analyzed various genetic systems comprising HIV-1, the malaria-causing parasite Plasmodium vivax , the fungus Aspergillus niger , and the TEM-family of β-lactamase associated with antibiotic resistance. For all systems, our approach revealed higher order interactions among mutations.

  7. An emergent approach to analogical inference

    Science.gov (United States)

    Thibodeau, Paul H.; Flusberg, Stephen J.; Glick, Jeremy J.; Sternberg, Daniel A.

    2013-03-01

    In recent years, a growing number of researchers have proposed that analogy is a core component of human cognition. According to the dominant theoretical viewpoint, analogical reasoning requires a specific suite of cognitive machinery, including explicitly coded symbolic representations and a mapping or binding mechanism that operates over these representations. Here we offer an alternative approach: we find that analogical inference can emerge naturally and spontaneously from a relatively simple, error-driven learning mechanism without the need to posit any additional analogy-specific machinery. The results also parallel findings from the developmental literature on analogy, demonstrating a shift from an initial reliance on surface feature similarity to the use of relational similarity later in training. Variants of the model allow us to consider and rule out alternative accounts of its performance. We conclude by discussing how these findings can potentially refine our understanding of the processes that are required to perform analogical inference.

  8. Pointwise probability reinforcements for robust statistical inference.

    Science.gov (United States)

    Frénay, Benoît; Verleysen, Michel

    2014-02-01

    Statistical inference using machine learning techniques may be difficult with small datasets because of abnormally frequent data (AFDs). AFDs are observations that are much more frequent in the training sample that they should be, with respect to their theoretical probability, and include e.g. outliers. Estimates of parameters tend to be biased towards models which support such data. This paper proposes to introduce pointwise probability reinforcements (PPRs): the probability of each observation is reinforced by a PPR and a regularisation allows controlling the amount of reinforcement which compensates for AFDs. The proposed solution is very generic, since it can be used to robustify any statistical inference method which can be formulated as a likelihood maximisation. Experiments show that PPRs can be easily used to tackle regression, classification and projection: models are freed from the influence of outliers. Moreover, outliers can be filtered manually since an abnormality degree is obtained for each observation. Copyright © 2013 Elsevier Ltd. All rights reserved.

  9. Statistical inference from imperfect photon detection

    International Nuclear Information System (INIS)

    Audenaert, Koenraad M R; Scheel, Stefan

    2009-01-01

    We consider the statistical properties of photon detection with imperfect detectors that exhibit dark counts and less than unit efficiency, in the context of tomographic reconstruction. In this context, the detectors are used to implement certain positive operator-valued measures (POVMs) that would allow us to reconstruct the quantum state or quantum process under consideration. Here we look at the intermediate step of inferring outcome probabilities from measured outcome frequencies, and show how this inference can be performed in a statistically sound way in the presence of detector imperfections. Merging outcome probabilities for different sets of POVMs into a consistent quantum state picture has been treated elsewhere (Audenaert and Scheel 2009 New J. Phys. 11 023028). Single-photon pulsed measurements as well as continuous wave measurements are covered.

  10. An Intuitive Dashboard for Bayesian Network Inference

    Science.gov (United States)

    Reddy, Vikas; Charisse Farr, Anna; Wu, Paul; Mengersen, Kerrie; Yarlagadda, Prasad K. D. V.

    2014-03-01

    Current Bayesian network software packages provide good graphical interface for users who design and develop Bayesian networks for various applications. However, the intended end-users of these networks may not necessarily find such an interface appealing and at times it could be overwhelming, particularly when the number of nodes in the network is large. To circumvent this problem, this paper presents an intuitive dashboard, which provides an additional layer of abstraction, enabling the end-users to easily perform inferences over the Bayesian networks. Unlike most software packages, which display the nodes and arcs of the network, the developed tool organises the nodes based on the cause-and-effect relationship, making the user-interaction more intuitive and friendly. In addition to performing various types of inferences, the users can conveniently use the tool to verify the behaviour of the developed Bayesian network. The tool has been developed using QT and SMILE libraries in C++.

  11. Working with sample data exploration and inference

    CERN Document Server

    Chaffe-Stengel, Priscilla

    2014-01-01

    Managers and analysts routinely collect and examine key performance measures to better understand their operations and make good decisions. Being able to render the complexity of operations data into a coherent account of significant events requires an understanding of how to work well with raw data and to make appropriate inferences. Although some statistical techniques for analyzing data and making inferences are sophisticated and require specialized expertise, there are methods that are understandable and applicable by anyone with basic algebra skills and the support of a spreadsheet package. By applying these fundamental methods themselves rather than turning over both the data and the responsibility for analysis and interpretation to an expert, managers will develop a richer understanding and potentially gain better control over their environment. This text is intended to describe these fundamental statistical techniques to managers, data analysts, and students. Statistical analysis of sample data is enh...

  12. Parametric inference for biological sequence analysis.

    Science.gov (United States)

    Pachter, Lior; Sturmfels, Bernd

    2004-11-16

    One of the major successes in computational biology has been the unification, by using the graphical model formalism, of a multitude of algorithms for annotating and comparing biological sequences. Graphical models that have been applied to these problems include hidden Markov models for annotation, tree models for phylogenetics, and pair hidden Markov models for alignment. A single algorithm, the sum-product algorithm, solves many of the inference problems that are associated with different statistical models. This article introduces the polytope propagation algorithm for computing the Newton polytope of an observation from a graphical model. This algorithm is a geometric version of the sum-product algorithm and is used to analyze the parametric behavior of maximum a posteriori inference calculations for graphical models.

  13. Inferences on Children’s Reading Groups

    Directory of Open Access Journals (Sweden)

    Javier González García

    2009-05-01

    Full Text Available This article focuses on the non-literal information of a text, which can be inferred from key elements or clues offered by the text itself. This kind of text is called implicit text or inference, due to the thinking process that it stimulates. The explicit resources that lead to information retrieval are related to others of implicit information, which have increased their relevance. In this study, during two courses, how two teachers interpret three stories and how they establish a debate dividing the class into three student groups, was analyzed. The sample was formed by two classes of two urban public schools of Burgos capital (Spain, and two of public schools of Tampico (Mexico. This allowed us to observe an increasing percentage value of the group focused in text comprehension, and a lesser percentage of the group perceiving comprehension as a secondary objective.

  14. Inferring Genetic Ancestry: Opportunities, Challenges, and Implications

    OpenAIRE

    Royal, Charmaine D.; Novembre, John; Fullerton, Stephanie M.; Goldstein, David B.; Long, Jeffrey C.; Bamshad, Michael J.; Clark, Andrew G.

    2010-01-01

    Increasing public interest in direct-to-consumer (DTC) genetic ancestry testing has been accompanied by growing concern about issues ranging from the personal and societal implications of the testing to the scientific validity of ancestry inference. The very concept of “ancestry” is subject to misunderstanding in both the general and scientific communities. What do we mean by ancestry? How exactly is ancestry measured? How far back can such ancestry be defined and by which genetic tools? How ...

  15. Inferring ontology graph structures using OWL reasoning

    KAUST Repository

    Rodriguez-Garcia, Miguel Angel

    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.

  16. Role of Speaker Cues in Attention Inference

    OpenAIRE

    Jin Joo Lee; Cynthia Breazeal; David DeSteno

    2017-01-01

    Current state-of-the-art approaches to emotion recognition primarily focus on modeling the nonverbal expressions of the sole individual without reference to contextual elements such as the co-presence of the partner. In this paper, we demonstrate that the accurate inference of listeners’ social-emotional state of attention depends on accounting for the nonverbal behaviors of their storytelling partner, namely their speaker cues. To gain a deeper understanding of the role of speaker cues in at...

  17. Inferring ontology graph structures using OWL reasoning.

    Science.gov (United States)

    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.

  18. Constrained bayesian inference of project performance models

    OpenAIRE

    Sunmola, Funlade

    2013-01-01

    Project performance models play an important role in the management of project success. When used for monitoring projects, they can offer predictive ability such as indications of possible delivery problems. Approaches for monitoring project performance relies on available project information including restrictions imposed on the project, particularly the constraints of cost, quality, scope and time. We study in this paper a Bayesian inference methodology for project performance modelling in ...

  19. Using metacognitive cues to infer others' thinking

    OpenAIRE

    André Mata; Tiago Almeida

    2014-01-01

    Three studies tested whether people use cues about the way other people think---for example, whether others respond fast vs. slow---to infer what responses other people might give to reasoning problems. People who solve reasoning problems using deliberative thinking have better insight than intuitive problem-solvers into the responses that other people might give to the same problems. Presumably because deliberative responders think of intuitive responses before they think o...

  20. Thermodynamics of statistical inference by cells.

    Science.gov (United States)

    Lang, Alex H; Fisher, Charles K; Mora, Thierry; Mehta, Pankaj

    2014-10-03

    The deep connection between thermodynamics, computation, and information is now well established both theoretically and experimentally. Here, we extend these ideas to show that thermodynamics also places fundamental constraints on statistical estimation and learning. To do so, we investigate the constraints placed by (nonequilibrium) thermodynamics on the ability of biochemical signaling networks to estimate the concentration of an external signal. We show that accuracy is limited by energy consumption, suggesting that there are fundamental thermodynamic constraints on statistical inference.

  1. Simple spatial scaling rules behind complex cities.

    Science.gov (United States)

    Li, Ruiqi; Dong, Lei; Zhang, Jiang; Wang, Xinran; Wang, Wen-Xu; Di, Zengru; Stanley, H Eugene

    2017-11-28

    Although most of wealth and innovation have been the result of human interaction and cooperation, we are not yet able to quantitatively predict the spatial distributions of three main elements of cities: population, roads, and socioeconomic interactions. By a simple model mainly based on spatial attraction and matching growth mechanisms, we reveal that the spatial scaling rules of these three elements are in a consistent framework, which allows us to use any single observation to infer the others. All numerical and theoretical results are consistent with empirical data from ten representative cities. In addition, our model can also provide a general explanation of the origins of the universal super- and sub-linear aggregate scaling laws and accurately predict kilometre-level socioeconomic activity. Our work opens a new avenue for uncovering the evolution of cities in terms of the interplay among urban elements, and it has a broad range of applications.

  2. SPATIAL DISTRIBUTION OF POVERTY AT DIFFERENT SCALES

    Directory of Open Access Journals (Sweden)

    Gandhi PAWITAN

    2010-01-01

    Full Text Available Poverty mapping is usually developed from some sources of data, such as from census and survey data. In some practical application, the poverty was measured usually by household income or expenditure of daily basic consumption. Using different scales and zoning on a particular set of spatial data may leads to problems in interpreting the results. In practice, organizations publish statistics and maps at a particular area level. Minot and Baulch (2005a discussed some consequences of using aggregated level data in poverty mapping, which may affect the validity of the output. The key point of this paper is to compare spatial distribution of the poverty at two different scale, which is the province and district level. How the spatial distribution of the poverty at province level can be use to infer the distribution at the district level. The geographical weighted regression will be applied, and the poverty data of Vietnam will be used as an illustration.

  3. Bootstrap inference when using multiple imputation.

    Science.gov (United States)

    Schomaker, Michael; Heumann, Christian

    2018-04-16

    Many modern estimators require bootstrapping to calculate confidence intervals because either no analytic standard error is available or the distribution of the parameter of interest is nonsymmetric. It remains however unclear how to obtain valid bootstrap inference when dealing with multiple imputation to address missing data. We present 4 methods that are intuitively appealing, easy to implement, and combine bootstrap estimation with multiple imputation. We show that 3 of the 4 approaches yield valid inference, but that the performance of the methods varies with respect to the number of imputed data sets and the extent of missingness. Simulation studies reveal the behavior of our approaches in finite samples. A topical analysis from HIV treatment research, which determines the optimal timing of antiretroviral treatment initiation in young children, demonstrates the practical implications of the 4 methods in a sophisticated and realistic setting. This analysis suffers from missing data and uses the g-formula for inference, a method for which no standard errors are available. Copyright © 2018 John Wiley & Sons, Ltd.

  4. Role of Speaker Cues in Attention Inference

    Directory of Open Access Journals (Sweden)

    Jin Joo Lee

    2017-10-01

    Full Text Available Current state-of-the-art approaches to emotion recognition primarily focus on modeling the nonverbal expressions of the sole individual without reference to contextual elements such as the co-presence of the partner. In this paper, we demonstrate that the accurate inference of listeners’ social-emotional state of attention depends on accounting for the nonverbal behaviors of their storytelling partner, namely their speaker cues. To gain a deeper understanding of the role of speaker cues in attention inference, we conduct investigations into real-world interactions of children (5–6 years old storytelling with their peers. Through in-depth analysis of human–human interaction data, we first identify nonverbal speaker cues (i.e., backchannel-inviting cues and listener responses (i.e., backchannel feedback. We then demonstrate how speaker cues can modify the interpretation of attention-related backchannels as well as serve as a means to regulate the responsiveness of listeners. We discuss the design implications of our findings toward our primary goal of developing attention recognition models for storytelling robots, and we argue that social robots can proactively use speaker cues to form more accurate inferences about the attentive state of their human partners.

  5. Cortical information flow during inferences of agency

    Directory of Open Access Journals (Sweden)

    Myrthel eDogge

    2014-08-01

    Full Text Available Building on the recent finding that agency experiences do not merely rely on sensorimotor information but also on cognitive cues, this exploratory study uses electroencephalographic recordings to examine functional connectivity during agency inference processing in a setting where action and outcome are independent. Participants completed a computerized task in which they pressed a button followed by one of two color words (red or blue and rated their experienced agency over producing the color. Before executing the action, a matching or mismatching color word was pre-activated by explicitly instructing participants to produce the color (goal condition or by briefly presenting the color word (prime condition. In both conditions, experienced agency was higher in matching versus mismatching trials. Furthermore, increased electroencephalography (EEG-based connectivity strength was observed between parietal and frontal nodes and within the (prefrontal cortex when color-outcomes matched with goals and participants reported high agency. This pattern of increased connectivity was not identified in trials where outcomes were pre-activated through primes. These results suggest that different connections are involved in the experience and in the loss of agency, as well as in inferences of agency resulting from different types of pre-activation. Moreover, the findings provide novel support for the involvement of a fronto-parietal network in agency inferences.

  6. Phylogenetic Inference of HIV Transmission Clusters

    Directory of Open Access Journals (Sweden)

    Vlad Novitsky

    2017-10-01

    Full Text Available Better understanding the structure and dynamics of HIV transmission networks is essential for designing the most efficient interventions to prevent new HIV transmissions, and ultimately for gaining control of the HIV epidemic. The inference of phylogenetic relationships and the interpretation of results rely on the definition of the HIV transmission cluster. The definition of the HIV cluster is complex and dependent on multiple factors, including the design of sampling, accuracy of sequencing, precision of sequence alignment, evolutionary models, the phylogenetic method of inference, and specified thresholds for cluster support. While the majority of studies focus on clusters, non-clustered cases could also be highly informative. A new dimension in the analysis of the global and local HIV epidemics is the concept of phylogenetically distinct HIV sub-epidemics. The identification of active HIV sub-epidemics reveals spreading viral lineages and may help in the design of targeted interventions.HIVclustering can also be affected by sampling density. Obtaining a proper sampling density may increase statistical power and reduce sampling bias, so sampling density should be taken into account in study design and in interpretation of phylogenetic results. Finally, recent advances in long-range genotyping may enable more accurate inference of HIV transmission networks. If performed in real time, it could both inform public-health strategies and be clinically relevant (e.g., drug-resistance testing.

  7. Causal inference of asynchronous audiovisual speech

    Directory of Open Access Journals (Sweden)

    John F Magnotti

    2013-11-01

    Full Text Available During speech perception, humans integrate auditory information from the voice with visual information from the face. This multisensory integration increases perceptual precision, but only if the two cues come from the same talker; this requirement has been largely ignored by current models of speech perception. We describe a generative model of multisensory speech perception that includes this critical step of determining the likelihood that the voice and face information have a common cause. A key feature of the model is that it is based on a principled analysis of how an observer should solve this causal inference problem using the asynchrony between two cues and the reliability of the cues. This allows the model to make predictions abut the behavior of subjects performing a synchrony judgment task, predictive power that does not exist in other approaches, such as post hoc fitting of Gaussian curves to behavioral data. We tested the model predictions against the performance of 37 subjects performing a synchrony judgment task viewing audiovisual speech under a variety of manipulations, including varying asynchronies, intelligibility, and visual cue reliability. The causal inference model outperformed the Gaussian model across two experiments, providing a better fit to the behavioral data with fewer parameters. Because the causal inference model is derived from a principled understanding of the task, model parameters are directly interpretable in terms of stimulus and subject properties.

  8. Functional neuroanatomy of intuitive physical inference.

    Science.gov (United States)

    Fischer, Jason; Mikhael, John G; Tenenbaum, Joshua B; Kanwisher, Nancy

    2016-08-23

    To engage with the world-to understand the scene in front of us, plan actions, and predict what will happen next-we must have an intuitive grasp of the world's physical structure and dynamics. How do the objects in front of us rest on and support each other, how much force would be required to move them, and how will they behave when they fall, roll, or collide? Despite the centrality of physical inferences in daily life, little is known about the brain mechanisms recruited to interpret the physical structure of a scene and predict how physical events will unfold. Here, in a series of fMRI experiments, we identified a set of cortical regions that are selectively engaged when people watch and predict the unfolding of physical events-a "physics engine" in the brain. These brain regions are selective to physical inferences relative to nonphysical but otherwise highly similar scenes and tasks. However, these regions are not exclusively engaged in physical inferences per se or, indeed, even in scene understanding; they overlap with the domain-general "multiple demand" system, especially the parts of that system involved in action planning and tool use, pointing to a close relationship between the cognitive and neural mechanisms involved in parsing the physical content of a scene and preparing an appropriate action.

  9. Elements of Causal Inference: Foundations and Learning Algorithms

    DEFF Research Database (Denmark)

    Peters, Jonas Martin; Janzing, Dominik; Schölkopf, Bernhard

    A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning......A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning...

  10. Integrating distributed Bayesian inference and reinforcement learning for sensor management

    NARCIS (Netherlands)

    Grappiolo, C.; Whiteson, S.; Pavlin, G.; Bakker, B.

    2009-01-01

    This paper introduces a sensor management approach that integrates distributed Bayesian inference (DBI) and reinforcement learning (RL). DBI is implemented using distributed perception networks (DPNs), a multiagent approach to performing efficient inference, while RL is used to automatically

  11. Spatial Management Areas

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Spatial management files combine all related and relevant spatial management files into an integrated fisheries management file. Overlaps of the redundant spatial...

  12. Bootstrapping phylogenies inferred from rearrangement data

    Directory of Open Access Journals (Sweden)

    Lin Yu

    2012-08-01

    Full Text Available Abstract Background Large-scale sequencing of genomes has enabled the inference of phylogenies based on the evolution of genomic architecture, under such events as rearrangements, duplications, and losses. Many evolutionary models and associated algorithms have been designed over the last few years and have found use in comparative genomics and phylogenetic inference. However, the assessment of phylogenies built from such data has not been properly addressed to date. The standard method used in sequence-based phylogenetic inference is the bootstrap, but it relies on a large number of homologous characters that can be resampled; yet in the case of rearrangements, the entire genome is a single character. Alternatives such as the jackknife suffer from the same problem, while likelihood tests cannot be applied in the absence of well established probabilistic models. Results We present a new approach to the assessment of distance-based phylogenetic inference from whole-genome data; our approach combines features of the jackknife and the bootstrap and remains nonparametric. For each feature of our method, we give an equivalent feature in the sequence-based framework; we also present the results of extensive experimental testing, in both sequence-based and genome-based frameworks. Through the feature-by-feature comparison and the experimental results, we show that our bootstrapping approach is on par with the classic phylogenetic bootstrap used in sequence-based reconstruction, and we establish the clear superiority of the classic bootstrap for sequence data and of our corresponding new approach for rearrangement data over proposed variants. Finally, we test our approach on a small dataset of mammalian genomes, verifying that the support values match current thinking about the respective branches. Conclusions Our method is the first to provide a standard of assessment to match that of the classic phylogenetic bootstrap for aligned sequences. Its

  13. Bootstrapping phylogenies inferred from rearrangement data.

    Science.gov (United States)

    Lin, Yu; Rajan, Vaibhav; Moret, Bernard Me

    2012-08-29

    Large-scale sequencing of genomes has enabled the inference of phylogenies based on the evolution of genomic architecture, under such events as rearrangements, duplications, and losses. Many evolutionary models and associated algorithms have been designed over the last few years and have found use in comparative genomics and phylogenetic inference. However, the assessment of phylogenies built from such data has not been properly addressed to date. The standard method used in sequence-based phylogenetic inference is the bootstrap, but it relies on a large number of homologous characters that can be resampled; yet in the case of rearrangements, the entire genome is a single character. Alternatives such as the jackknife suffer from the same problem, while likelihood tests cannot be applied in the absence of well established probabilistic models. We present a new approach to the assessment of distance-based phylogenetic inference from whole-genome data; our approach combines features of the jackknife and the bootstrap and remains nonparametric. For each feature of our method, we give an equivalent feature in the sequence-based framework; we also present the results of extensive experimental testing, in both sequence-based and genome-based frameworks. Through the feature-by-feature comparison and the experimental results, we show that our bootstrapping approach is on par with the classic phylogenetic bootstrap used in sequence-based reconstruction, and we establish the clear superiority of the classic bootstrap for sequence data and of our corresponding new approach for rearrangement data over proposed variants. Finally, we test our approach on a small dataset of mammalian genomes, verifying that the support values match current thinking about the respective branches. Our method is the first to provide a standard of assessment to match that of the classic phylogenetic bootstrap for aligned sequences. Its support values follow a similar scale and its receiver

  14. Electromagnetism of Bacterial Growth

    Science.gov (United States)

    Ainiwaer, Ailiyasi

    2011-10-01

    There has been increasing concern from the public about personal health due to the significant rise in the daily use of electrical devices such as cell phones, radios, computers, GPS, video games and television. All of these devices create electromagnetic (EM) fields, which are simply magnetic and electric fields surrounding the appliances that simultaneously affect the human bio-system. Although these can affect the human system, obstacles can easily shield or weaken the electrical fields; however, magnetic fields cannot be weakened and can pass through walls, human bodies and most other objects. The present study was conducted to examine the possible effects of bacteria when exposed to magnetic fields. The results indicate that a strong causal relationship is not clear, since different magnetic fields affect the bacteria differently, with some causing an increase in bacterial cells, and others causing a decrease in the same cells. This phenomenon has yet to be explained, but the current study attempts to offer a mathematical explanation for this occurrence. The researchers added cultures to the magnetic fields to examine any effects to ion transportation. Researchers discovered ions such as potassium and sodium are affected by the magnetic field. A formula is presented in the analysis section to explain this effect.

  15. Type Inference for Session Types in the Pi-Calculus

    DEFF Research Database (Denmark)

    Graversen, Eva Fajstrup; Harbo, Jacob Buchreitz; Huttel, Hans

    2014-01-01

    In this paper we present a direct algorithm for session type inference for the π-calculus. Type inference for session types has previously been achieved by either imposing limitations and restriction on the π-calculus, or by reducing the type inference problem to that for linear types. Our approach...

  16. Reasoning about Informal Statistical Inference: One Statistician's View

    Science.gov (United States)

    Rossman, Allan J.

    2008-01-01

    This paper identifies key concepts and issues associated with the reasoning of informal statistical inference. I focus on key ideas of inference that I think all students should learn, including at secondary level as well as tertiary. I argue that a fundamental component of inference is to go beyond the data at hand, and I propose that statistical…

  17. Statistical Inference at Work: Statistical Process Control as an Example

    Science.gov (United States)

    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…

  18. Bacterial growth on macrophyte leachate and fate of bacterial production

    International Nuclear Information System (INIS)

    Findlay, S.; Carlough, L.; Crocker, M.T.; Gill, H.K.; Meyer, J.L.; Smith, P.J.

    1986-01-01

    The role bacteria play in transferring organic carbon to other trophic levels in aquatic ecosystems depends on the efficiency with which they convert dissolved organic [ 14 C]-labelled carbon into bacterial biomass and on the ability of consumers to graze bacteria. The authors have measured the conversion efficiency for bacteria growing on macrophyte-derived dissolved organic carbon and estimated the amount of bacterial production removed by grazing. Bacteria converted this DOC into new tissue with an efficiency of 53%, substantially higher than the apparent conversion efficiency of macrophyte-derived particulate organic carbon or other types of DOC. Two estimates of grazing indicate that the decline in bacterial numbers after the bloom was probably due to grazing by flagellates. These results show the significance of the bacterial link between DOC and other trophic levels

  19. Modeling physiological resistance in bacterial biofilms.

    Science.gov (United States)

    Cogan, N G; Cortez, Ricardo; Fauci, Lisa

    2005-07-01

    A mathematical model of the action of antimicrobial agents on bacterial biofilms is presented. The model includes the fluid dynamics in and around the biofilm, advective and diffusive transport of two chemical constituents and the mechanism of physiological resistance. Although the mathematical model applies in three dimensions, we present two-dimensional simulations for arbitrary biofilm domains and various dosing strategies. The model allows the prediction of the spatial evolution of bacterial population and chemical constituents as well as different dosing strategies based on the fluid motion. We find that the interaction between the nutrient and the antimicrobial agent can reproduce survival curves which are comparable to other model predictions as well as experimental results. The model predicts that exposing the biofilm to low concentration doses of antimicrobial agent for longer time is more effective than short time dosing with high antimicrobial agent concentration. The effects of flow reversal and the roughness of the fluid/biofilm are also investigated. We find that reversing the flow increases the effectiveness of dosing. In addition, we show that overall survival decreases with increasing surface roughness.

  20. Hierarchical modeling and inference in ecology: The analysis of data from populations, metapopulations and communities

    Science.gov (United States)

    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.

  1. Molecular detection of human bacterial pathogens

    National Research Council Canada - National Science Library

    Liu, Dongyou

    2011-01-01

    .... Molecular Detection of Human Bacterial Pathogens addresses this issue, with international scientists in respective bacterial pathogen research and diagnosis providing expert summaries on current...

  2. Is there a hierarchy of social inferences? The likelihood and speed of inferring intentionality, mind, and personality.

    Science.gov (United States)

    Malle, Bertram F; Holbrook, Jess

    2012-04-01

    People interpret behavior by making inferences about agents' intentionality, mind, and personality. Past research studied such inferences 1 at a time; in real life, people make these inferences simultaneously. The present studies therefore examined whether 4 major inferences (intentionality, desire, belief, and personality), elicited simultaneously in response to an observed behavior, might be ordered in a hierarchy of likelihood and speed. To achieve generalizability, the studies included a wide range of stimulus behaviors, presented them verbally and as dynamic videos, and assessed inferences both in a retrieval paradigm (measuring the likelihood and speed of accessing inferences immediately after they were made) and in an online processing paradigm (measuring the speed of forming inferences during behavior observation). Five studies provide evidence for a hierarchy of social inferences-from intentionality and desire to belief to personality-that is stable across verbal and visual presentations and that parallels the order found in developmental and primate research. (c) 2012 APA, all rights reserved.

  3. Inferring animal social networks and leadership: applications for passive monitoring arrays.

    Science.gov (United States)

    Jacoby, David M P; Papastamatiou, Yannis P; Freeman, Robin

    2016-11-01

    Analyses of animal social networks have frequently benefited from techniques derived from other disciplines. Recently, machine learning algorithms have been adopted to infer social associations from time-series data gathered using remote, telemetry systems situated at provisioning sites. We adapt and modify existing inference methods to reveal the underlying social structure of wide-ranging marine predators moving through spatial arrays of passive acoustic receivers. From six months of tracking data for grey reef sharks (Carcharhinus amblyrhynchos) at Palmyra atoll in the Pacific Ocean, we demonstrate that some individuals emerge as leaders within the population and that this behavioural coordination is predicted by both sex and the duration of co-occurrences between conspecifics. In doing so, we provide the first evidence of long-term, spatially extensive social processes in wild sharks. To achieve these results, we interrogate simulated and real tracking data with the explicit purpose of drawing attention to the key considerations in the use and interpretation of inference methods and their impact on resultant social structure. We provide a modified translation of the GMMEvents method for R, including new analyses quantifying the directionality and duration of social events with the aim of encouraging the careful use of these methods more widely in less tractable social animal systems but where passive telemetry is already widespread. © 2016 The Authors.

  4. The inference from a single case: moral versus scientific inferences in implementing new biotechnologies.

    Science.gov (United States)

    Hofmann, B

    2008-06-01

    Are there similarities between scientific and moral inference? This is the key question in this article. It takes as its point of departure an instance of one person's story in the media changing both Norwegian public opinion and a brand-new Norwegian law prohibiting the use of saviour siblings. The case appears to falsify existing norms and to establish new ones. The analysis of this case reveals similarities in the modes of inference in science and morals, inasmuch as (a) a single case functions as a counter-example to an existing rule; (b) there is a common presupposition of stability, similarity and order, which makes it possible to reason from a few cases to a general rule; and (c) this makes it possible to hold things together and retain order. In science, these modes of inference are referred to as falsification, induction and consistency. In morals, they have a variety of other names. Hence, even without abandoning the fact-value divide, there appear to be similarities between inference in science and inference in morals, which may encourage communication across the boundaries between "the two cultures" and which are relevant to medical humanities.

  5. Microbial Ecophysiology of Whey Biomethanation: Characterization of Bacterial Trophic Populations and Prevalent Species in Continuous Culture

    OpenAIRE

    Chartrain, M.; Zeikus, J. G.

    1986-01-01

    The organization and species composition of bacterial trophic groups associated with lactose biomethanation were investigated in a whey-processing chemostat by enumeration, isolation, and general characterization studies. The bacteria were spatially organized as free-living forms and as self-immobilized forms appearing in flocs. Three dominant bacterial trophic group populations were present (in most probable number per milliliter) whose species numbers varied with the substrate consumed: hyd...

  6. The Bayesian group lasso for confounded spatial data

    Science.gov (United States)

    Hefley, Trevor J.; Hooten, Mevin B.; Hanks, Ephraim M.; Russell, Robin E.; Walsh, Daniel P.

    2017-01-01

    Generalized linear mixed models for spatial processes are widely used in applied statistics. In many applications of the spatial generalized linear mixed model (SGLMM), the goal is to obtain inference about regression coefficients while achieving optimal predictive ability. When implementing the SGLMM, multicollinearity among covariates and the spatial random effects can make computation challenging and influence inference. We present a Bayesian group lasso prior with a single tuning parameter that can be chosen to optimize predictive ability of the SGLMM and jointly regularize the regression coefficients and spatial random effect. We implement the group lasso SGLMM using efficient Markov chain Monte Carlo (MCMC) algorithms and demonstrate how multicollinearity among covariates and the spatial random effect can be monitored as a derived quantity. To test our method, we compared several parameterizations of the SGLMM using simulated data and two examples from plant ecology and disease ecology. In all examples, problematic levels multicollinearity occurred and influenced sampling efficiency and inference. We found that the group lasso prior resulted in roughly twice the effective sample size for MCMC samples of regression coefficients and can have higher and less variable predictive accuracy based on out-of-sample data when compared to the standard SGLMM.

  7. Highly Heterogeneous Soil Bacterial Communities around Terra Nova Bay of Northern Victoria Land, Antarctica

    Science.gov (United States)

    Lim, Hyoun Soo; Hong, Soon Gyu; Kim, Ji Hee; Lee, Joohan; Choi, Taejin; Ahn, Tae Seok; Kim, Ok-Sun

    2015-01-01

    Given the diminished role of biotic interactions in soils of continental Antarctica, abiotic factors are believed to play a dominant role in structuring of microbial communities. However, many ice-free regions remain unexplored, and it is unclear which environmental gradients are primarily responsible for the variations among bacterial communities. In this study, we investigated the soil bacterial community around Terra Nova Bay of Victoria Land by pyrosequencing and determined which environmental variables govern the bacterial community structure at the local scale. Six bacterial phyla, Actinobacteria, Proteobacteria, Acidobacteria, Chloroflexi, Cyanobacteria, and Bacteroidetes, were dominant, but their relative abundance varied greatly across locations. Bacterial community structures were affected little by spatial distance, but structured more strongly by site, which was in accordance with the soil physicochemical compositions. At both the phylum and species levels, bacterial community structure was explained primarily by pH and water content, while certain earth elements and trace metals also played important roles in shaping community variation. The higher heterogeneity of the bacterial community structure found at this site indicates how soil bacterial communities have adapted to different compositions of edaphic variables under extreme environmental conditions. Taken together, these findings greatly advance our understanding of the adaption of soil bacterial populations to this harsh environment. PMID:25799273

  8. Neurological sequelae of bacterial meningitis

    NARCIS (Netherlands)

    Lucas, Marjolein J.; Brouwer, Matthijs C.; van de Beek, Diederik

    2016-01-01

    We reported on occurrence and impact of neurological sequelae after bacterial meningitis. We reviewed occurrence of neurological sequelae in children and adults after pneumococcal and meningococcal meningitis. Most frequently reported sequelae are focal neurological deficits, hearing loss, cognitive

  9. Bacterial tracheitis in Down's syndrome.

    OpenAIRE

    Cant, A J; Gibson, P J; West, R J

    1987-01-01

    Four children with Down's syndrome and bacterial tracheitis are described. In three the infection was due to Haemophilus influenza. In patients with Down's syndrome presenting with stridor tracheitis should be considered and appropriate treatment started.

  10. Bacterial Communities: Interactions to Scale

    Directory of Open Access Journals (Sweden)

    Reed M. Stubbendieck

    2016-08-01

    Full Text Available In the environment, bacteria live in complex multispecies communities. These communities span in scale from small, multicellular aggregates to billions or trillions of cells within the gastrointestinal tract of animals. The dynamics of bacterial communities are determined by pairwise interactions that occur between different species in the community. Though interactions occur between a few cells at a time, the outcomes of these interchanges have ramifications that ripple through many orders of magnitude, and ultimately affect the macroscopic world including the health of host organisms. In this review we cover how bacterial competition influences the structures of bacterial communities. We also emphasize methods and insights garnered from culture-dependent pairwise interaction studies, metagenomic analyses, and modeling experiments. Finally, we argue that the integration of multiple approaches will be instrumental to future understanding of the underlying dynamics of bacterial communities.

  11. Bacterial flora of sturgeon fingerling

    International Nuclear Information System (INIS)

    Arani, A.S.; Mosahab, R.

    2008-01-01

    The study on microbial populations is a suitable tool to understand and apply control methods to improve the sanitary level of production in fish breeding and rearing centers, ensure health of sturgeon fingerlings at the time of their release into the rivers and also in the conversation and restoration of these valuable stocks in the Caspian Sea, Iran. A laboratory research based on Austin methods (Austin, B., Austin, D.A. 1993) was conducted for bacterial study on 3 sturgeon species naming A. persicus, A. stellatus and A. nudiventris during different growth stages. Bacterial flora of Acinetobacter, Moraxella, Aeromonas, Vibrio, Edwardsiella, Staphylococcus, Proteus, Yersinia, Pseudomonas and Plesiomonas were determined. The factors which may induce changes in bacterial populations during different stages of fife are the followings: quality of water in rearing ponds, different conditions for growth stages, suitable time for colonization of bacterial flora in rearing pond, water temperature increase in fingerlings size and feeding condition. (author)

  12. Bacterial translocation: impact of probiotics

    OpenAIRE

    Jeppsson, Bengt; Mangell, Peter; Adawi, Diya; Molin, Göran

    2004-01-01

    There is a considerable amount of data in humans showing that patients who cannot take in nutrients enterally have more organ failure in the intensive care unit, a less favourable prognosis, and a higher frequency of septicaemia, in particular involving bacterial species from the intestinal tract. However, there is little evidence that this is connected with translocation of bacterial species in humans. Animal data more uniformly imply the existence of such a connection. The main focus of thi...

  13. Bacterial cellulose/boehmite composites

    International Nuclear Information System (INIS)

    Salvi, Denise T.B. de; Barud, Hernane S.; Messaddeq, Younes; Ribeiro, Sidney J.L.; Caiut, Jose Mauricio A.

    2011-01-01

    Composites based on bacterial cellulose membranes and boehmite were obtained. SEM results indicate that the bacterial cellulose (BC) membranes are totally covered by boehmite and obtained XRD patterns suggest structural changes due to this boehmite addition. Thermal stability is accessed through TG curves and is dependent on boehmite content. Transparency is high comparing to pure BC as can be seen through UV-vis absorption spectroscopy. (author)

  14. Investigation of multimodal forward scatter phenotyping from bacterial colonies

    Science.gov (United States)

    Kim, Huisung

    A rapid, label-free, and elastic light scattering (ELS) based bacterial colony phenotyping technology, bacterial rapid detection using optical scattering technology (BARDOT) provides a successful classification of several bacterial genus and species. For a thorough understanding of the phenomena and overcoming the limitations of the previous design, five additional modalities from a bacterial colony: 3D morphology, spatial optical density (OD) distribution, spectral forward scattering pattern, spectral OD, and surface backward reflection pattern are proposed to enhance the classification/identification ratio, and the feasibilities of each modality are verified. For the verification, three different instruments: integrated colony morphology analyzer (ICMA), multi-spectral BARDOT (MS-BARDOT) , and multi-modal BARDOT (MM-BARDOT) are proposed and developed. The ICMA can measure 3D morphology and spatial OD distribution of the colony simultaneously. A commercialized confocal displacement meter is used to measure the profiles of the bacterial colonies, together with a custom built optical density measurement unit to interrogate the biophysics behind the collective behavior of a bacterial colony. The system delivers essential information related to the quantitative growth dynamics (height, diameter, aspect ratio, optical density) of the bacterial colony, as well as, a relationship in between the morphological characteristics of the bacterial colony and its forward scattering pattern. Two different genera: Escherichia coli O157:H7 EDL933, and Staphylococcus aureus ATCC 25923 are selected for the analysis of the spatially resolved growth dynamics, while, Bacillus spp. such as B. subtilis ATCC 6633, B. cereus ATCC 14579, B. thuringiensis DUP6044, B. polymyxa B719W, and B. megaterium DSP 81319, are interrogated since some of the Bacillus spp. provides strikingly different characteristics of ELS patterns, and the origin of the speckle patterns are successfully correlated with

  15. Nonparametric inference of network structure and dynamics

    Science.gov (United States)

    Peixoto, Tiago P.

    The network structure of complex systems determine their function and serve as evidence for the evolutionary mechanisms that lie behind them. Despite considerable effort in recent years, it remains an open challenge to formulate general descriptions of the large-scale structure of network systems, and how to reliably extract such information from data. Although many approaches have been proposed, few methods attempt to gauge the statistical significance of the uncovered structures, and hence the majority cannot reliably separate actual structure from stochastic fluctuations. Due to the sheer size and high-dimensionality of many networks, this represents a major limitation that prevents meaningful interpretations of the results obtained with such nonstatistical methods. In this talk, I will show how these issues can be tackled in a principled and efficient fashion by formulating appropriate generative models of network structure that can have their parameters inferred from data. By employing a Bayesian description of such models, the inference can be performed in a nonparametric fashion, that does not require any a priori knowledge or ad hoc assumptions about the data. I will show how this approach can be used to perform model comparison, and how hierarchical models yield the most appropriate trade-off between model complexity and quality of fit based on the statistical evidence present in the data. I will also show how this general approach can be elegantly extended to networks with edge attributes, that are embedded in latent spaces, and that change in time. The latter is obtained via a fully dynamic generative network model, based on arbitrary-order Markov chains, that can also be inferred in a nonparametric fashion. Throughout the talk I will illustrate the application of the methods with many empirical networks such as the internet at the autonomous systems level, the global airport network, the network of actors and films, social networks, citations among

  16. Impact of noise on molecular network inference.

    Directory of Open Access Journals (Sweden)

    Radhakrishnan Nagarajan

    Full Text Available Molecular entities work in concert as a system and mediate phenotypic outcomes and disease states. There has been recent interest in modelling the associations between molecular entities from their observed expression profiles as networks using a battery of algorithms. These networks have proven to be useful abstractions of the underlying pathways and signalling mechanisms. Noise is ubiquitous in molecular data and can have a pronounced effect on the inferred network. Noise can be an outcome of several factors including: inherent stochastic mechanisms at the molecular level, variation in the abundance of molecules, heterogeneity, sensitivity of the biological assay or measurement artefacts prevalent especially in high-throughput settings. The present study investigates the impact of discrepancies in noise variance on pair-wise dependencies, conditional dependencies and constraint-based Bayesian network structure learning algorithms that incorporate conditional independence tests as a part of the learning process. Popular network motifs and fundamental connections, namely: (a common-effect, (b three-chain, and (c coherent type-I feed-forward loop (FFL are investigated. The choice of these elementary networks can be attributed to their prevalence across more complex networks. Analytical expressions elucidating the impact of discrepancies in noise variance on pairwise dependencies and conditional dependencies for special cases of these motifs are presented. Subsequently, the impact of noise on two popular constraint-based Bayesian network structure learning algorithms such as Grow-Shrink (GS and Incremental Association Markov Blanket (IAMB that implicitly incorporate tests for conditional independence is investigated. Finally, the impact of noise on networks inferred from publicly available single cell molecular expression profiles is investigated. While discrepancies in noise variance are overlooked in routine molecular network inference, the

  17. Bayesian Estimation and Inference using Stochastic Hardware

    Directory of Open Access Journals (Sweden)

    Chetan Singh Thakur

    2016-03-01

    Full Text Available In this paper, we present the implementation of two types of Bayesian inference problems to demonstrate the potential of building probabilistic algorithms in hardware using single set of building blocks with the ability to perform these computations in real time. The first implementation, referred to as the BEAST (Bayesian Estimation and Stochastic Tracker, demonstrates a simple problem where an observer uses an underlying Hidden Markov Model (HMM to track a target in one dimension. In this implementation, sensors make noisy observations of the target position at discrete time steps. The tracker learns the transition model for target movement, and the observation model for the noisy sensors, and uses these to estimate the target position by solving the Bayesian recursive equation online. We show the tracking performance of the system and demonstrate how it can learn the observation model, the transition model, and the external distractor (noise probability interfering with the observations. In the second implementation, referred to as the Bayesian INference in DAG (BIND, we show how inference can be performed in a Directed Acyclic Graph (DAG using stochastic circuits. We show how these building blocks can be easily implemented using simple digital logic gates. An advantage of the stochastic electronic implementation is that it is robust to certain types of noise, which may become an issue in integrated circuit (IC technology with feature sizes in the order of tens of nanometers due to their low noise margin, the effect of high-energy cosmic rays and the low supply voltage. In our framework, the flipping of random individual bits would not affect the system performance because information is encoded in a bit stream.

  18. Bayesian Estimation and Inference Using Stochastic Electronics.

    Science.gov (United States)

    Thakur, Chetan Singh; Afshar, Saeed; Wang, Runchun M; Hamilton, Tara J; Tapson, Jonathan; van Schaik, André

    2016-01-01

    In this paper, we present the implementation of two types of Bayesian inference problems to demonstrate the potential of building probabilistic algorithms in hardware using single set of building blocks with the ability to perform these computations in real time. The first implementation, referred to as the BEAST (Bayesian Estimation and Stochastic Tracker), demonstrates a simple problem where an observer uses an underlying Hidden Markov Model (HMM) to track a target in one dimension. In this implementation, sensors make noisy observations of the target position at discrete time steps. The tracker learns the transition model for target movement, and the observation model for the noisy sensors, and uses these to estimate the target position by solving the Bayesian recursive equation online. We show the tracking performance of the system and demonstrate how it can learn the observation model, the transition model, and the external distractor (noise) probability interfering with the observations. In the second implementation, referred to as the Bayesian INference in DAG (BIND), we show how inference can be performed in a Directed Acyclic Graph (DAG) using stochastic circuits. We show how these building blocks can be easily implemented using simple digital logic gates. An advantage of the stochastic electronic implementation is that it is robust to certain types of noise, which may become an issue in integrated circuit (IC) technology with feature sizes in the order of tens of nanometers due to their low noise margin, the effect of high-energy cosmic rays and the low supply voltage. In our framework, the flipping of random individual bits would not affect the system performance because information is encoded in a bit stream.

  19. Spatially Controlled Relay Beamforming

    Science.gov (United States)

    Kalogerias, Dionysios

    layered channel model previously described, this line of research has resulted in a number of general, asymptotic convergence results, advancing the theory of grid-based approximate nonlinear stochastic filtering. In particular, sufficient conditions, ensuring asymptotic optimality are relaxed, and, at the same time, the mode of convergence is strengthened. Although the need for such results initiated as an attempt to theoretically characterize the performance of the proposed approximate methods for statistical inference, in regard to the proposed channel modeling approach, they turn out to be of fundamental importance in the areas of nonlinear estimation and stochastic control. The experimental validation of the proposed channel model, as well as the related parameter estimation problem, termed as "Markovian Channel Profiling (MCP)", fundamentally important for any practical deployment, are subject of current, ongoing research. Second, adopting the first of the two aforementioned channel modeling approaches, we consider the spatially controlled relay beamforming problem for an AF network with a single source, a single destination, and multiple, controlled at will, relay nodes. (Abstract shortened by ProQuest.).

  20. Arsenic uptake in bacterial calcite

    Science.gov (United States)

    Catelani, Tiziano; Perito, Brunella; Bellucci, Francesco; Lee, Sang Soo; Fenter, Paul; Newville, Matthew; Rimondi, Valentina; Pratesi, Giovanni; Costagliola, Pilario

    2018-02-01

    Bio-mediated processes for arsenic (As) uptake in calcite were investigated by means of X-ray Diffraction (XRD) and X-ray Absorption Spectroscopy (XAS) coupled with X-ray Fluorescence measurements. The environmental bacterial strain Bacillus licheniformis BD5, sampled at the Bullicame Hot Springs (Viterbo, Central Italy), was used to synthesize calcite from As-enriched growth media. Both liquid and solid cultures were applied to simulate planktonic and biofilm community environments, respectively. Bacterial calcite samples cultured in liquid media had an As enrichment factor (Kd) 50 times higher than that from solid media. The XRD analysis revealed an elongation of the crystal lattice along the c axis (by 0.03 Å) for biogenic calcite, which likely resulted from the substitution of larger arsenate for carbonate in the crystal. The XAS data also showed a clear difference in the oxidation state of sorbed As between bacterial and abiotic calcite. Abiotic chemical processes yielded predominantly As(V) uptake whereas bacterial precipitation processes led to the uptake of both As(III) and As(V). The presence of As(III) in bacterial calcite is proposed to result from subsequent reduction of arsenate to arsenite by bacterial activities. To the best of our knowledge, this is the first experimental observation of the incorporation of As(III) in the calcite crystal lattice, revealing a critical role of biochemical processes for the As cycling in nature.

  1. Arsenic uptake in bacterial calcite

    Energy Technology Data Exchange (ETDEWEB)

    Catelani, Tiziano; Perito, Brunella; Bellucci, Francesco; Lee, Sang Soo; Fenter, Paul; Newville, Matthew G.; Rimondi, Valentina; Pratesi, Giovanni; Costagliola, Pilario

    2018-02-01

    Bio-mediated processes for arsenic (As) uptake in calcite were investigated by means of X-ray Diffraction (XRD) and Xray Absorption Spectroscopy (XAS) coupled with X-ray Fluorescence measurements. The environmental bacterial strain Bacillus licheniformis BD5, sampled at the Bullicame Hot Springs (Viterbo, Central Italy), was used to synthesize calcite from As-enriched growth media. Both liquid and solid cultures were applied to simulate planktonic and biofilm community environments, respectively. Bacterial calcite samples cultured in liquid media had an As enrichment factor (Kd) 50 times higher than that from solid media. The XRD analysis revealed an elongation of the crystal lattice along the c axis (by 0.03Å) for biogenic calcite, which likely resulted from the substitution of larger arsenate for carbonate in the crystal. The XAS data also showed a clear difference in the oxidation state of sorbed As between bacterial and abiotic calcite. Abiotic chemical processes yielded predominantly As(V) uptake whereas bacterial precipitation processes led to the uptake of both As(III) and As(V). The presence of As(III) in bacterial calcite is proposed to result from subsequent reduction of arsenate to arsenite by bacterial activities. To the best of our knowledge, this is the first experimental observation of the incorporation of As(III) in the calcite crystal lattice, revealing a critical role of biochemical processes for the As cycling in nature.

  2. Bacmeta: simulator for genomic evolution in bacterial metapopulations.

    Science.gov (United States)

    Sipola, Aleksi; Marttinen, Pekka; Corander, Jukka

    2018-02-20

    The advent of genomic data from densely sampled bacterial populations has created a need for flexible simulators by which models and hypotheses can be efficiently investigated in the light of empirical observations. Bacmeta provides fast stochastic simulation of neutral evolution within a large collection of interconnected bacterial populations with completely adjustable connectivity network. Stochastic events of mutations, recombinations, insertions/deletions, migrations and microepidemics can be simulated in discrete non-overlapping generations with a Wright-Fisher model that operates on explicit sequence data of any desired genome length. Each model component, including locus, bacterial strain, population, and ultimately the whole metapopulation, is efficiently simulated using C ++ objects, and detailed metadata from each level can be acquired. The software can be executed in a cluster environment using simple textual input files, enabling, e.g., large-scale simulations and likelihood-free inference. Bacmeta is implemented with C ++ for Linux, Mac and Windows. It is available at https://bitbucket.org/aleksisipola/bacmeta under the BSD 3-clause license. aleksi.sipola@helsinki.fi, jukka.corander@medisin.uio.no. Supplementary data are available at Bioinformatics online.

  3. Using absolute x-ray spectral measurements to infer stagnation conditions in ICF implosions

    Science.gov (United States)

    Patel, Pravesh; Benedetti, L. R.; Cerjan, C.; Clark, D. S.; Hurricane, O. A.; Izumi, N.; Jarrott, L. C.; Khan, S.; Kritcher, A. L.; Ma, T.; Macphee, A. G.; Landen, O.; Spears, B. K.; Springer, P. T.

    2016-10-01

    Measurements of the continuum x-ray spectrum emitted from the hot-spot of an ICF implosion can be used to infer a number thermodynamic properties at stagnation including temperature, pressure, and hot-spot mix. In deuterium-tritium (DT) layered implosion experiments on the National Ignition Facility (NIF) we field a number of x-ray diagnostics that provide spatial, temporal, and spectrally-resolved measurements of the radiated x-ray emission. We report on analysis of these measurements using a 1-D hot-spot model to infer thermodynamic properties at stagnation. We compare these to similar properties that can be derived from DT fusion neutron measurements. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

  4. Children's and adults' judgments of the certainty of deductive inferences, inductive inferences, and guesses.

    Science.gov (United States)

    Pillow, Bradford H; Pearson, Raeanne M; Hecht, Mary; Bremer, Amanda

    2010-01-01

    Children and adults rated their own certainty following inductive inferences, deductive inferences, and guesses. Beginning in kindergarten, participants rated deductions as more certain than weak inductions or guesses. Deductions were rated as more certain than strong inductions beginning in Grade 3, and fourth-grade children and adults differentiated strong inductions, weak inductions, and informed guesses from pure guesses. By Grade 3, participants also gave different types of explanations for their deductions and inductions. These results are discussed in relation to children's concepts of cognitive processes, logical reasoning, and epistemological development.

  5. Robust Inference with Multi-way Clustering

    OpenAIRE

    A. Colin Cameron; Jonah B. Gelbach; Douglas L. Miller; Doug Miller

    2009-01-01

    In this paper we propose a variance estimator for the OLS estimator as well as for nonlinear estimators such as logit, probit and GMM. This variance estimator enables cluster-robust inference when there is two-way or multi-way clustering that is non-nested. The variance estimator extends the standard cluster-robust variance estimator or sandwich estimator for one-way clustering (e.g. Liang and Zeger (1986), Arellano (1987)) and relies on similar relatively weak distributional assumptions. Our...

  6. Approximate Inference and Deep Generative Models

    CERN Multimedia

    CERN. Geneva

    2018-01-01

    Advances in deep generative models are at the forefront of deep learning research because of the promise they offer for allowing data-efficient learning, and for model-based reinforcement learning. In this talk I'll review a few standard methods for approximate inference and introduce modern approximations which allow for efficient large-scale training of a wide variety of generative models. Finally, I'll demonstrate several important application of these models to density estimation, missing data imputation, data compression and planning.

  7. Abductive Inference using Array-Based Logic

    DEFF Research Database (Denmark)

    Frisvad, Jeppe Revall; Falster, Peter; Møller, Gert L.

    The notion of abduction has found its usage within a wide variety of AI fields. Computing abductive solutions has, however, shown to be highly intractable in logic programming. To avoid this intractability we present a new approach to logicbased abduction; through the geometrical view of data...... employed in array-based logic we embrace abduction in a simple structural operation. We argue that a theory of abduction on this form allows for an implementation which, at runtime, can perform abductive inference quite efficiently on arbitrary rules of logic representing knowledge of finite domains....

  8. Generic Patch Inference

    DEFF Research Database (Denmark)

    Andersen, Jesper; Lawall, Julia Laetitia

    2008-01-01

    A key issue in maintaining Linux device drivers is the need to update drivers in response to evolutions in Linux internal libraries. Currently, there is little tool support for performing and documenting such changes. In this paper we present a tool, spfind, that identifies common changes made...... developers can use it to extract an abstract representation of the set of changes that others have made. Our experiments on recent changes in Linux show that the inferred generic patches are more concise than the corresponding patches found in commits to the Linux source tree while being safe with respect...

  9. Inverse Ising Inference Using All the Data

    Science.gov (United States)

    Aurell, Erik; Ekeberg, Magnus

    2012-03-01

    We show that a method based on logistic regression, using all the data, solves the inverse Ising problem far better than mean-field calculations relying only on sample pairwise correlation functions, while still computationally feasible for hundreds of nodes. The largest improvement in reconstruction occurs for strong interactions. Using two examples, a diluted Sherrington-Kirkpatrick model and a two-dimensional lattice, we also show that interaction topologies can be recovered from few samples with good accuracy and that the use of l1 regularization is beneficial in this process, pushing inference abilities further into low-temperature regimes.

  10. Bacterial cell wall preservation during organic matter diagenesis in sediments off Peru

    DEFF Research Database (Denmark)

    Lomstein, Bente Aagaard; Niggemann, Jutta; Jørgensen, Bo Barker

    BACTERIAL CELL WALL PRESERVATION DURING ORGANIC MATTER DIAGENESIS IN SEDIMENTS OFF PERU The spatial distribution of total hydrolysable amino acids, total hydrolysable amino sugars and amino acid enantiomers (D- and L-forms) were investigated in surface sediments at 20 stations in the Peru margin: 9......°45 S - 13º32 S. The objective of this study was to assess the preservation of bacterial cell walls during diagenesis of organic matter. Bacterial cell walls were traced by analysis of biomarkers uniquely produced by bacteria (D-amino acids and muramic acid). The diagenetic status of the sediments......:00 Presentation is given by student: No...

  11. Spatial econometrics using microdata

    CERN Document Server

    Dubé, Jean

    2014-01-01

    This book provides an introduction to spatial analyses concerning disaggregated (or micro) spatial data.Particular emphasis is put on spatial data compilation and the structuring of the connections between the observations. Descriptive analysis methods of spatial data are presented in order to identify and measure the spatial, global and local dependency.The authors then focus on autoregressive spatial models, to control the problem of spatial dependency between the residues of a basic linear statistical model, thereby contravening one of the basic hypotheses of the ordinary least squares appr

  12. Modelling within-host spatiotemporal dynamics of invasive bacterial disease.

    Directory of Open Access Journals (Sweden)

    Andrew J Grant

    2008-04-01

    Full Text Available Mechanistic determinants of bacterial growth, death, and spread within mammalian hosts cannot be fully resolved studying a single bacterial population. They are also currently poorly understood. Here, we report on the application of sophisticated experimental approaches to map spatiotemporal population dynamics of bacteria during an infection. We analyzed heterogeneous traits of simultaneous infections with tagged Salmonella enterica populations (wild-type isogenic tagged strains [WITS] in wild-type and gene-targeted mice. WITS are phenotypically identical but can be distinguished and enumerated by quantitative PCR, making it possible, using probabilistic models, to estimate bacterial death rate based on the disappearance of strains through time. This multidisciplinary approach allowed us to establish the timing, relative occurrence, and immune control of key infection parameters in a true host-pathogen combination. Our analyses support a model in which shortly after infection, concomitant death and rapid bacterial replication lead to the establishment of independent bacterial subpopulations in different organs, a process controlled by host antimicrobial mechanisms. Later, decreased microbial mortality leads to an exponential increase in the number of bacteria that spread locally, with subsequent mixing of bacteria between organs via bacteraemia and further stochastic selection. This approach provides us with an unprecedented outlook on the pathogenesis of S. enterica infections, illustrating the complex spatial and stochastic effects that drive an infectious disease. The application of the novel method that we present in appropriate and diverse host-pathogen combinations, together with modelling of the data that result, will facilitate a comprehensive view of the spatial and stochastic nature of within-host dynamics.

  13. Modeling and inferring cleavage patterns in proliferating epithelia.

    Directory of Open Access Journals (Sweden)

    Ankit B Patel

    2009-06-01

    Full Text Available The regulation of cleavage plane orientation is one of the key mechanisms driving epithelial morphogenesis. Still, many aspects of the relationship between local cleavage patterns and tissue-level properties remain poorly understood. Here we develop a topological model that simulates the dynamics of a 2D proliferating epithelium from generation to generation, enabling the exploration of a wide variety of biologically plausible cleavage patterns. We investigate a spectrum of models that incorporate the spatial impact of neighboring cells and the temporal influence of parent cells on the choice of cleavage plane. Our findings show that cleavage patterns generate "signature" equilibrium distributions of polygonal cell shapes. These signatures enable the inference of local cleavage parameters such as neighbor impact, maternal influence, and division symmetry from global observations of the distribution of cell shape. Applying these insights to the proliferating epithelia of five diverse organisms, we find that strong division symmetry and moderate neighbor/maternal influence are required to reproduce the predominance of hexagonal cells and low variability in cell shape seen empirically. Furthermore, we present two distinct cleavage pattern models, one stochastic and one deterministic, that can reproduce the empirical distribution of cell shapes. Although the proliferating epithelia of the five diverse organisms show a highly conserved cell shape distribution, there are multiple plausible cleavage patterns that can generate this distribution, and experimental evidence suggests that indeed plants and fruitflies use distinct division mechanisms.

  14. Chemistry of groundwater discharge inferred from longitudinal river sampling

    Science.gov (United States)

    Batlle-Aguilar, J.; Harrington, G. A.; Leblanc, M.; Welch, C.; Cook, P. G.

    2014-02-01

    We present an approach for identifying groundwater discharge chemistry and quantifying spatially distributed groundwater discharge into rivers based on longitudinal synoptic sampling and flow gauging of a river. The method is demonstrated using a 450 km reach of a tropical river in Australia. Results obtained from sampling for environmental tracers, major ions, and selected trace element chemistry were used to calibrate a steady state one-dimensional advective transport model of tracer distribution along the river. The model closely reproduced river discharge and environmental tracer and chemistry composition along the study length. It provided a detailed longitudinal profile of groundwater inflow chemistry and discharge rates, revealing that regional fractured mudstones in the central part of the catchment contributed up to 40% of all groundwater discharge. Detailed analysis of model calibration errors and modeled/measured groundwater ion ratios elucidated that groundwater discharging in the top of the catchment is a mixture of local groundwater and bank storage return flow, making the method potentially useful to differentiate between local and regional sourced groundwater discharge. As the error in tracer concentration induced by a flow event applies equally to any conservative tracer, we show that major ion ratios can still be resolved with minimal error when river samples are collected during transient flow conditions. The ability of the method to infer groundwater inflow chemistry from longitudinal river sampling is particularly attractive in remote areas where access to groundwater is limited or not possible, and for identification of actual fluxes of salts and/or specific contaminant sources.

  15. Fingering instabilities in bacterial community phototaxis

    Science.gov (United States)

    Vps, Ritwika; Man Wah Chau, Rosanna; Casey Huang, Kerwyn; Gopinathan, Ajay

    Synechocystis sp PCC 6803 is a phototactic cyanobacterium that moves directionally in response to a light source. During phototaxis, these bacterial communities show emergent spatial organisation resulting in the formation of finger-like projections at the propagating front. In this study, we propose an analytical model that elucidates the underlying physical mechanisms which give rise to these spatial patterns. We describe the migrating front during phototaxis as a one-dimensional curve by considering the effects of phototactic bias, diffusion and surface tension. By considering the propagating front as composed of perturbations to a flat solution and using linear stability analysis, we predict a critical bias above which the finger-like projections appear as instabilities. We also predict the wavelengths of the fastest growing mode and the critical mode above which the instabilities disappear. We validate our predictions through comparisons to experimental data obtained by analysing images of phototaxis in Synechocystis communities. Our model also predicts the observed loss of instabilities in taxd1 mutants (cells with inactive TaxD1, an important photoreceptor in finger formation), by considering diffusion in mutually perpendicular directions and a lower, negative bias.

  16. Bacterial Prostatitis: Bacterial Virulence, Clinical Outcomes, and New Directions.

    Science.gov (United States)

    Krieger, John N; Thumbikat, Praveen

    2016-02-01

    Four prostatitis syndromes are recognized clinically: acute bacterial prostatitis, chronic bacterial prostatitis, chronic prostatitis/chronic pelvic pain syndrome, and asymptomatic prostatitis. Because Escherichia coli represents the most common cause of bacterial prostatitis, we investigated the importance of bacterial virulence factors and antimicrobial resistance in E. coli strains causing prostatitis and the potential association of these characteristics with clinical outcomes. A structured literature review revealed that we have limited understanding of the virulence-associated characteristics of E. coli causing acute prostatitis. Therefore, we completed a comprehensive microbiological and molecular investigation of a unique strain collection isolated from healthy young men. We also considered new data from an animal model system suggesting certain E. coli might prove important in the etiology of chronic prostatitis/chronic pelvic pain syndrome. Our human data suggest that E. coli needs multiple pathogenicity-associated traits to overcome anatomic and immune responses in healthy young men without urological risk factors. The phylogenetic background and accumulation of an exceptional repertoire of extraintestinal pathogenic virulence-associated genes indicate that these E. coli strains belong to a highly virulent subset of uropathogenic variants. In contrast, antibiotic resistance confers little added advantage to E. coli strains in these healthy outpatients. Our animal model data also suggest that certain pathogenic E. coli may be important in the etiology of chronic prostatitis/chronic pelvic pain syndrome through mechanisms that are dependent on the host genetic background and the virulence of the bacterial strain.

  17. Quantum Enhanced Inference in Markov Logic Networks.

    Science.gov (United States)

    Wittek, Peter; Gogolin, Christian

    2017-04-19

    Markov logic networks (MLNs) reconcile two opposing schools in machine learning and artificial intelligence: causal networks, which account for uncertainty extremely well, and first-order logic, which allows for formal deduction. An MLN is essentially a first-order logic template to generate Markov networks. Inference in MLNs is probabilistic and it is often performed by approximate methods such as Markov chain Monte Carlo (MCMC) Gibbs sampling. An MLN has many regular, symmetric structures that can be exploited at both first-order level and in the generated Markov network. We analyze the graph structures that are produced by various lifting methods and investigate the extent to which quantum protocols can be used to speed up Gibbs sampling with state preparation and measurement schemes. We review different such approaches, discuss their advantages, theoretical limitations, and their appeal to implementations. We find that a straightforward application of a recent result yields exponential speedup compared to classical heuristics in approximate probabilistic inference, thereby demonstrating another example where advanced quantum resources can potentially prove useful in machine learning.

  18. Inferring network topology from complex dynamics

    International Nuclear Information System (INIS)

    Shandilya, Srinivas Gorur; Timme, Marc

    2011-01-01

    Inferring the network topology from dynamical observations is a fundamental problem pervading research on complex systems. Here, we present a simple, direct method for inferring the structural connection topology of a network, given an observation of one collective dynamical trajectory. The general theoretical framework is applicable to arbitrary network dynamical systems described by ordinary differential equations. No interference (external driving) is required and the type of dynamics is hardly restricted in any way. In particular, the observed dynamics may be arbitrarily complex; stationary, invariant or transient; synchronous or asynchronous and chaotic or periodic. Presupposing a knowledge of the functional form of the dynamical units and of the coupling functions between them, we present an analytical solution to the inverse problem of finding the network topology from observing a time series of state variables only. Robust reconstruction is achieved in any sufficiently long generic observation of the system. We extend our method to simultaneously reconstructing both the entire network topology and all parameters appearing linear in the system's equations of motion. Reconstruction of network topology and system parameters is viable even in the presence of external noise that distorts the original dynamics substantially. The method provides a conceptually new step towards reconstructing a variety of real-world networks, including gene and protein interaction networks and neuronal circuits.

  19. Inferring climate sensitivity from volcanic events

    Energy Technology Data Exchange (ETDEWEB)

    Boer, G.J. [Environment Canada, University of Victoria, Canadian Centre for Climate Modelling and Analysis, Victoria, BC (Canada); Stowasser, M.; Hamilton, K. [University of Hawaii, International Pacific Research Centre, Honolulu, HI (United States)

    2007-04-15

    The possibility of estimating the equilibrium climate sensitivity of the earth-system from observations following explosive volcanic eruptions is assessed in the context of a perfect model study. Two modern climate models (the CCCma CGCM3 and the NCAR CCSM2) with different equilibrium climate sensitivities are employed in the investigation. The models are perturbed with the same transient volcano-like forcing and the responses analysed to infer climate sensitivities. For volcano-like forcing the global mean surface temperature responses of the two models are very similar, despite their differing equilibrium climate sensitivities, indicating that climate sensitivity cannot be inferred from the temperature record alone even if the forcing is known. Equilibrium climate sensitivities can be reasonably determined only if both the forcing and the change in heat storage in the system are known very accurately. The geographic patterns of clear-sky atmosphere/surface and cloud feedbacks are similar for both the transient volcano-like and near-equilibrium constant forcing simulations showing that, to a considerable extent, the same feedback processes are invoked, and determine the climate sensitivity, in both cases. (orig.)

  20. Facility Activity Inference Using Radiation Networks

    Energy Technology Data Exchange (ETDEWEB)

    Rao, Nageswara S. [ORNL; Ramirez Aviles, Camila A. [ORNL

    2017-11-01

    We consider the problem of inferring the operational status of a reactor facility using measurements from a radiation sensor network deployed around the facility’s ventilation off-gas stack. The intensity of stack emissions decays with distance, and the sensor counts or measurements are inherently random with parameters determined by the intensity at the sensor’s location. We utilize the measurements to estimate the intensity at the stack, and use it in a one-sided Sequential Probability Ratio Test (SPRT) to infer on/off status of the reactor. We demonstrate the superior performance of this method over conventional majority fusers and individual sensors using (i) test measurements from a network of 21 NaI detectors, and (ii) effluence measurements collected at the stack of a reactor facility. We also analytically establish the superior detection performance of the network over individual sensors with fixed and adaptive thresholds by utilizing the Poisson distribution of the counts. We quantify the performance improvements of the network detection over individual sensors using the packing number of the intensity space.