Interspecific bacterial interactions are reflected in multispecies biofilm spatial organization

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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.

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.

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

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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

Spatial Patterning of Newly-Inserted Material during Bacterial Cell Growth

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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.

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

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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

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

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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

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

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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

Meta-learning framework applied in bioinformatics inference system design.

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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.

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

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

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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.

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

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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

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

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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.

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

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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.

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

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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.

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

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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.

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

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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.

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

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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.

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

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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.

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

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

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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.

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

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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.

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

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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.

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

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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.

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

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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.

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.

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...

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...

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....

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

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

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

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.

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...

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...

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

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.

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.

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

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

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.

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...

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....

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...

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....

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.

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.

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.

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

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

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

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.

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

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

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

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.

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.

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.

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.

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.

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

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

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

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

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

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...

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

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...

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

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.

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.

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.

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.

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.

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.

Spatial modelling with R-INLA: A review

KAUST Repository

Bakka, Haakon; Rue, Haavard; Fuglstad, Geir-Arne; Riebler, Andrea; Bolin, David; Krainski, Elias; Simpson, Daniel; Lindgren, Finn

2018-01-01

Coming up with Bayesian models for spatial data is easy, but performing inference with them can be challenging. Writing fast inference code for a complex spatial model with realistically-sized datasets from scratch is time-consuming, and if changes are made to the model, there is little guarantee that the code performs well. The key advantages of R-INLA are the ease with which complex models can be created and modified, without the need to write complex code, and the speed at which inference can be done even for spatial problems with hundreds of thousands of observations. R-INLA handles latent Gaussian models, where fixed effects, structured and unstructured Gaussian random effects are combined linearly in a linear predictor, and the elements of the linear predictor are observed through one or more likelihoods. The structured random effects can be both standard areal model such as the Besag and the BYM models, and geostatistical models from a subset of the Mat\\'ern Gaussian random fields. In this review, we discuss the large success of spatial modelling with R-INLA and the types of spatial models that can be fitted, we give an overview of recent developments for areal models, and we give an overview of the stochastic partial differential equation (SPDE) approach and some of the ways it can be extended beyond the assumptions of isotropy and separability. In particular, we describe how slight changes to the SPDE approach leads to straight-forward approaches for non-stationary spatial models and non-separable space-time models.

Spatial modelling with R-INLA: A review

KAUST Repository

Bakka, Haakon

2018-02-18

Coming up with Bayesian models for spatial data is easy, but performing inference with them can be challenging. Writing fast inference code for a complex spatial model with realistically-sized datasets from scratch is time-consuming, and if changes are made to the model, there is little guarantee that the code performs well. The key advantages of R-INLA are the ease with which complex models can be created and modified, without the need to write complex code, and the speed at which inference can be done even for spatial problems with hundreds of thousands of observations. R-INLA handles latent Gaussian models, where fixed effects, structured and unstructured Gaussian random effects are combined linearly in a linear predictor, and the elements of the linear predictor are observed through one or more likelihoods. The structured random effects can be both standard areal model such as the Besag and the BYM models, and geostatistical models from a subset of the Mat\\\\\\'ern Gaussian random fields. In this review, we discuss the large success of spatial modelling with R-INLA and the types of spatial models that can be fitted, we give an overview of recent developments for areal models, and we give an overview of the stochastic partial differential equation (SPDE) approach and some of the ways it can be extended beyond the assumptions of isotropy and separability. In particular, we describe how slight changes to the SPDE approach leads to straight-forward approaches for non-stationary spatial models and non-separable space-time models.

Temporal and Spatial Dynamics of Sediment Anaerobic Ammonium Oxidation (Anammox) Bacteria in Freshwater Lakes.

Science.gov (United States)

Yang, Yuyin; Dai, Yu; Li, Ningning; Li, Bingxin; Xie, Shuguang; Liu, Yong

2017-02-01

Anaerobic ammonium-oxidizing (anammox) process can play an important role in freshwater nitrogen cycle. However, the distribution of anammox bacteria in freshwater lake and the associated environmental factors remain essentially unclear. The present study investigated the temporal and spatial dynamics of sediment anammox bacterial populations in eutrotrophic Dianchi Lake and mesotrophic Erhai Lake on the Yunnan Plateau (southwestern China). The remarkable spatial change of anammox bacterial abundance was found in Dianchi Lake, while the relatively slight spatial shift occurred in Erhai Lake. Dianchi Lake had greater anammox bacterial abundance than Erhai Lake. In both Dianchi Lake and Erhai Lake, anammox bacteria were much more abundant in summer than in spring. Anammox bacterial community richness, diversity, and structure in these two freshwater lakes were subjected to temporal and spatial variations. Sediment anammox bacterial communities in Dianchi Lake and Erhai Lake were dominated by Candidatus Brocadia and a novel phylotype followed by Candidatus Kuenenia; however, these two lakes had distinct anammox bacterial community structure. In addition, trophic status determined sediment anammox bacterial community structure.

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.

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.

Identifying essential genes in bacterial metabolic networks with machine learning methods

Science.gov (United States)

2010-01-01

Background Identifying essential genes in bacteria supports to identify potential drug targets and an understanding of minimal requirements for a synthetic cell. However, experimentally assaying the essentiality of their coding genes is resource intensive and not feasible for all bacterial organisms, in particular if they are infective. Results We developed a machine learning technique to identify essential genes using the experimental data of genome-wide knock-out screens from one bacterial organism to infer essential genes of another related bacterial organism. We used a broad variety of topological features, sequence characteristics and co-expression properties potentially associated with essentiality, such as flux deviations, centrality, codon frequencies of the sequences, co-regulation and phyletic retention. An organism-wise cross-validation on bacterial species yielded reliable results with good accuracies (area under the receiver-operator-curve of 75% - 81%). Finally, it was applied to drug target predictions for Salmonella typhimurium. We compared our predictions to the viability of experimental knock-outs of S. typhimurium and identified 35 enzymes, which are highly relevant to be considered as potential drug targets. Specifically, we detected promising drug targets in the non-mevalonate pathway. Conclusions Using elaborated features characterizing network topology, sequence information and microarray data enables to predict essential genes from a bacterial reference organism to a related query organism without any knowledge about the essentiality of genes of the query organism. In general, such a method is beneficial for inferring drug targets when experimental data about genome-wide knockout screens is not available for the investigated organism. PMID:20438628

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

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...

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

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

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...

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...

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

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.

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.

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.

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....

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...

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.

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.

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...

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.

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

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.

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.

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

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

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.

Bacterial community diversity of the deep-sea octocoral Paramuricea placomus

Directory of Open Access Journals (Sweden)

Christina A. Kellogg

2016-09-01

Full Text Available Compared to tropical corals, much less is known about deep-sea coral biology and ecology. Although the microbial communities of some deep-sea corals have been described, this is the first study to characterize the bacterial community associated with the deep-sea octocoral, Paramuricea placomus. Samples from five colonies of P. placomus were collected from Baltimore Canyon (379–382 m depth in the Atlantic Ocean off the east coast of the United States of America. DNA was extracted from the coral samples and 16S rRNA gene amplicons were pyrosequenced using V4-V5 primers. Three samples sequenced deeply (>4,000 sequences each and were further analyzed. The dominant microbial phylum was Proteobacteria, but other major phyla included Firmicutes and Planctomycetes. A conserved community of bacterial taxa held in common across the three P. placomus colonies was identified, comprising 68–90% of the total bacterial community depending on the coral individual. The bacterial community of P. placomus does not appear to include the genus Endozoicomonas, which has been found previously to be the dominant bacterial associate in several temperate and tropical gorgonians. Inferred functionality suggests the possibility of nitrogen cycling by the core bacterial community.

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.

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.

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.

Cortical hierarchies perform Bayesian causal inference in multisensory perception.

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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.

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....

Tapered composite likelihood for spatial max-stable models

KAUST Repository

Sang, Huiyan

2014-05-01

Spatial extreme value analysis is useful to environmental studies, in which extreme value phenomena are of interest and meaningful spatial patterns can be discerned. Max-stable process models are able to describe such phenomena. This class of models is asymptotically justified to characterize the spatial dependence among extremes. However, likelihood inference is challenging for such models because their corresponding joint likelihood is unavailable and only bivariate or trivariate distributions are known. In this paper, we propose a tapered composite likelihood approach by utilizing lower dimensional marginal likelihoods for inference on parameters of various max-stable process models. We consider a weighting strategy based on a "taper range" to exclude distant pairs or triples. The "optimal taper range" is selected to maximize various measures of the Godambe information associated with the tapered composite likelihood function. This method substantially reduces the computational cost and improves the efficiency over equally weighted composite likelihood estimators. We illustrate its utility with simulation experiments and an analysis of rainfall data in Switzerland.

Tapered composite likelihood for spatial max-stable models

KAUST Repository

Sang, Huiyan; Genton, Marc G.

2014-01-01

Spatial extreme value analysis is useful to environmental studies, in which extreme value phenomena are of interest and meaningful spatial patterns can be discerned. Max-stable process models are able to describe such phenomena. This class of models is asymptotically justified to characterize the spatial dependence among extremes. However, likelihood inference is challenging for such models because their corresponding joint likelihood is unavailable and only bivariate or trivariate distributions are known. In this paper, we propose a tapered composite likelihood approach by utilizing lower dimensional marginal likelihoods for inference on parameters of various max-stable process models. We consider a weighting strategy based on a "taper range" to exclude distant pairs or triples. The "optimal taper range" is selected to maximize various measures of the Godambe information associated with the tapered composite likelihood function. This method substantially reduces the computational cost and improves the efficiency over equally weighted composite likelihood estimators. We illustrate its utility with simulation experiments and an analysis of rainfall data in Switzerland.

Evaluating Bayesian spatial methods for modelling species distributions with clumped and restricted occurrence data.

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David W Redding

Full Text Available Statistical approaches for inferring the spatial distribution of taxa (Species Distribution Models, SDMs commonly rely on available occurrence data, which is often clumped and geographically restricted. Although available SDM methods address some of these factors, they could be more directly and accurately modelled using a spatially-explicit approach. Software to fit models with spatial autocorrelation parameters in SDMs are now widely available, but whether such approaches for inferring SDMs aid predictions compared to other methodologies is unknown. Here, within a simulated environment using 1000 generated species' ranges, we compared the performance of two commonly used non-spatial SDM methods (Maximum Entropy Modelling, MAXENT and boosted regression trees, BRT, to a spatial Bayesian SDM method (fitted using R-INLA, when the underlying data exhibit varying combinations of clumping and geographic restriction. Finally, we tested how any recommended methodological settings designed to account for spatially non-random patterns in the data impact inference. Spatial Bayesian SDM method was the most consistently accurate method, being in the top 2 most accurate methods in 7 out of 8 data sampling scenarios. Within high-coverage sample datasets, all methods performed fairly similarly. When sampling points were randomly spread, BRT had a 1-3% greater accuracy over the other methods and when samples were clumped, the spatial Bayesian SDM method had a 4%-8% better AUC score. Alternatively, when sampling points were restricted to a small section of the true range all methods were on average 10-12% less accurate, with greater variation among the methods. Model inference under the recommended settings to account for autocorrelation was not impacted by clumping or restriction of data, except for the complexity of the spatial regression term in the spatial Bayesian model. Methods, such as those made available by R-INLA, can be successfully used to account

Evaluating Bayesian spatial methods for modelling species distributions with clumped and restricted occurrence data.

Science.gov (United States)

Redding, David W; Lucas, Tim C D; Blackburn, Tim M; Jones, Kate E

2017-01-01

Statistical approaches for inferring the spatial distribution of taxa (Species Distribution Models, SDMs) commonly rely on available occurrence data, which is often clumped and geographically restricted. Although available SDM methods address some of these factors, they could be more directly and accurately modelled using a spatially-explicit approach. Software to fit models with spatial autocorrelation parameters in SDMs are now widely available, but whether such approaches for inferring SDMs aid predictions compared to other methodologies is unknown. Here, within a simulated environment using 1000 generated species' ranges, we compared the performance of two commonly used non-spatial SDM methods (Maximum Entropy Modelling, MAXENT and boosted regression trees, BRT), to a spatial Bayesian SDM method (fitted using R-INLA), when the underlying data exhibit varying combinations of clumping and geographic restriction. Finally, we tested how any recommended methodological settings designed to account for spatially non-random patterns in the data impact inference. Spatial Bayesian SDM method was the most consistently accurate method, being in the top 2 most accurate methods in 7 out of 8 data sampling scenarios. Within high-coverage sample datasets, all methods performed fairly similarly. When sampling points were randomly spread, BRT had a 1-3% greater accuracy over the other methods and when samples were clumped, the spatial Bayesian SDM method had a 4%-8% better AUC score. Alternatively, when sampling points were restricted to a small section of the true range all methods were on average 10-12% less accurate, with greater variation among the methods. Model inference under the recommended settings to account for autocorrelation was not impacted by clumping or restriction of data, except for the complexity of the spatial regression term in the spatial Bayesian model. Methods, such as those made available by R-INLA, can be successfully used to account for spatial

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.

Novel probabilistic models of spatial genetic ancestry with applications to stratification correction in genome-wide association studies.

Science.gov (United States)

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

2017-03-15

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

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.

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.

Hierarchical spatial models for predicting pygmy rabbit distribution and relative abundance

Science.gov (United States)

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

2010-01-01

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

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.

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...

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

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

KAUST Repository

Tagle, Felipe

2017-12-06

Large, non-Gaussian spatial datasets pose a considerable modeling challenge as the dependence structure implied by the model needs to be captured at different scales, while retaining feasible inference. Skew-normal and skew-t distributions have only 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 presents the first multi-resolution spatial model inspired by the skew-t distribution, where a large-scale effect follows a multivariate normal distribution and the fine-scale effects follow a multivariate skew-normal distributions. The resulting marginal distribution for each region is skew-t, thereby allowing for greater flexibility in capturing skewness and heavy tails characterizing many environmental datasets. Likelihood-based inference is performed using a Monte Carlo EM algorithm. The model is applied as a stochastic generator of daily wind speeds over Saudi Arabia.

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.

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

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

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.

Spatial vulnerability assessments by regression kriging

Science.gov (United States)

Pásztor, László; Laborczi, Annamária; Takács, Katalin; Szatmári, Gábor

2016-04-01

Two fairly different complex environmental phenomena, causing natural hazard were mapped based on a combined spatial inference approach. The behaviour is related to various environmental factors and the applied approach enables the inclusion of several, spatially exhaustive auxiliary variables that are available for mapping. Inland excess water (IEW) is an interrelated natural and human induced phenomenon causes several problems in the flat-land regions of Hungary, which cover nearly half of the country. The term 'inland excess water' refers to the occurrence of inundations outside the flood levee that originate from sources differing from flood overflow, it is surplus surface water forming due to the lack of runoff, insufficient absorption capability of soil or the upwelling of groundwater. There is a multiplicity of definitions, which indicate the complexity of processes that govern this phenomenon. Most of the definitions have a common part, namely, that inland excess water is temporary water inundation that occurs in flat-lands due to both precipitation and groundwater emerging on the surface as substantial sources. Radon gas is produced in the radioactive decay chain of uranium, which is an element that is naturally present in soils. Radon is transported mainly by diffusion and convection mechanisms through the soil depending mainly on soil physical and meteorological parameters and can enter and accumulate in the buildings. Health risk originating from indoor radon concentration attributed to natural factors is characterized by geogenic radon potential (GRP). In addition to geology and meteorology, physical soil properties play significant role in the determination of GRP. Identification of areas with high risk requires spatial modelling, that is mapping of specific natural hazards. In both cases external environmental factors determine the behaviour of the target process (occurrence/frequncy of IEW and grade of GRP respectively). Spatial auxiliary

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.

Incidence of bacterial diseases associated with irrigation methods on onions (Allium cepa).

Science.gov (United States)

Chorolque, A; Pozzo Ardizzi, C; Pellejero, G; Aschkar, G; García Navarro, F J; Jiménez Ballesta, R

2018-04-24

In the last decade, diseases of bacterial origin in onions have increased and this has led to significant losses in production. These diseases are currently observed in both the Old and New Worlds. The aim of the experimental work reported here was to evaluate whether the irrigation method influences the incidence of diseases of bacterial origin. In cases where the inoculum was natural, the initial incidence of Soft Bacterial Rot was not manifested in any treatment in the first year, whereas at the end of the conservation period all treatments had increased incidences of infection. Sprinkler irrigation (8%) was statistically differentiated from the other treatments, for which the final incidence was similar (4.5%). For all irrigation treatments, the final incidence of Bacterial Soft Rot decreased or remained stable towards the end of the cycle, with the exception of sprinkler irrigation in 2015, which increased. It can be inferred from the results that the irrigation method does have an influence on the incidence of diseases of bacterial origin in the post-harvest stage for onions. This article is protected by copyright. All rights reserved.

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.

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.

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

A national prediction model for PM2.5 component exposures and measurement error-corrected health effect inference.

Science.gov (United States)

Bergen, Silas; Sheppard, Lianne; Sampson, Paul D; Kim, Sun-Young; Richards, Mark; Vedal, Sverre; Kaufman, Joel D; Szpiro, Adam A

2013-09-01

Studies estimating health effects of long-term air pollution exposure often use a two-stage approach: building exposure models to assign individual-level exposures, which are then used in regression analyses. This requires accurate exposure modeling and careful treatment of exposure measurement error. To illustrate the importance of accounting for exposure model characteristics in two-stage air pollution studies, we considered a case study based on data from the Multi-Ethnic Study of Atherosclerosis (MESA). We built national spatial exposure models that used partial least squares and universal kriging to estimate annual average concentrations of four PM2.5 components: elemental carbon (EC), organic carbon (OC), silicon (Si), and sulfur (S). We predicted PM2.5 component exposures for the MESA cohort and estimated cross-sectional associations with carotid intima-media thickness (CIMT), adjusting for subject-specific covariates. We corrected for measurement error using recently developed methods that account for the spatial structure of predicted exposures. Our models performed well, with cross-validated R2 values ranging from 0.62 to 0.95. Naïve analyses that did not account for measurement error indicated statistically significant associations between CIMT and exposure to OC, Si, and S. EC and OC exhibited little spatial correlation, and the corrected inference was unchanged from the naïve analysis. The Si and S exposure surfaces displayed notable spatial correlation, resulting in corrected confidence intervals (CIs) that were 50% wider than the naïve CIs, but that were still statistically significant. The impact of correcting for measurement error on health effect inference is concordant with the degree of spatial correlation in the exposure surfaces. Exposure model characteristics must be considered when performing two-stage air pollution epidemiologic analyses because naïve health effect inference may be inappropriate.

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.

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

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.

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.

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.

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.

Optimization of analytical parameters for inferring relationships among Escherichia coli isolates from repetitive-element PCR by maximizing correspondence with multilocus sequence typing data.

Science.gov (United States)

Goldberg, Tony L; Gillespie, Thomas R; Singer, Randall S

2006-09-01

Repetitive-element PCR (rep-PCR) is a method for genotyping bacteria based on the selective amplification of repetitive genetic elements dispersed throughout bacterial chromosomes. The method has great potential for large-scale epidemiological studies because of its speed and simplicity; however, objective guidelines for inferring relationships among bacterial isolates from rep-PCR data are lacking. We used multilocus sequence typing (MLST) as a "gold standard" to optimize the analytical parameters for inferring relationships among Escherichia coli isolates from rep-PCR data. We chose 12 isolates from a large database to represent a wide range of pairwise genetic distances, based on the initial evaluation of their rep-PCR fingerprints. We conducted MLST with these same isolates and systematically varied the analytical parameters to maximize the correspondence between the relationships inferred from rep-PCR and those inferred from MLST. Methods that compared the shapes of densitometric profiles ("curve-based" methods) yielded consistently higher correspondence values between data types than did methods that calculated indices of similarity based on shared and different bands (maximum correspondences of 84.5% and 80.3%, respectively). Curve-based methods were also markedly more robust in accommodating variations in user-specified analytical parameter values than were "band-sharing coefficient" methods, and they enhanced the reproducibility of rep-PCR. Phylogenetic analyses of rep-PCR data yielded trees with high topological correspondence to trees based on MLST and high statistical support for major clades. These results indicate that rep-PCR yields accurate information for inferring relationships among E. coli isolates and that accuracy can be enhanced with the use of analytical methods that consider the shapes of densitometric profiles.

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.

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.

An evaluation for spatial resolution, using a single target on a medical image

Energy Technology Data Exchange (ETDEWEB)

Lee, Kyung Sung [Dept. of Radiotechnology, Cheju Halla University, Cheju (Korea, Republic of)

2016-12-15

Hitherto, spatial resolution has commonly been evaluated by test patterns or phantoms built on some specific distances (from close to far) between two objects (or double targets). This evaluation method's shortcoming is that resolution is restricted to target distances of phantoms made for test. Therefore, in order to solve the problem, this study proposes and verifies a new method to efficiently test spatial resolution with a single target. For the research I used PSF and JND to propose an idea to measure spatial resolution. After that, I made experiments by commonly used phantoms to verify my new evaluation hypothesis inferred from the above method. To analyse the hypothesis, I used LabVIEW program and got a line pixel from digital image. The result was identical to my spatial-resolution hypothesis inferred from a single target. The findings of the experiment proves only a single target can be enough to relatively evaluate spatial resolution on a digital image. In other words, the limit of the traditional spatial-resolution evaluation method, based on double targets, can be overcome by my new evaluation one using a single target.

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.

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...

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

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.

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.

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....

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...

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.

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

Surface radiant flux densities inferred from LAC and GAC AVHRR data

Science.gov (United States)

Berger, F.; Klaes, D.

To infer surface radiant flux densities from current (NOAA-AVHRR, ERS-1/2 ATSR) and future meteorological (Envisat AATSR, MSG, METOP) satellite data, the complex, modular analysis scheme SESAT (Strahlungs- und Energieflüsse aus Satellitendaten) could be developed (Berger, 2001). This scheme allows the determination of cloud types, optical and microphysical cloud properties as well as surface and TOA radiant flux densities. After testing of SESAT in Central Europe and the Baltic Sea catchment (more than 400scenes U including a detailed validation with various surface measurements) it could be applied to a large number of NOAA-16 AVHRR overpasses covering the globe.For the analysis, two different spatial resolutions U local area coverage (LAC) andwere considered. Therefore, all inferred results, like global area coverage (GAC) U cloud cover, cloud properties and radiant properties, could be intercompared. Specific emphasis could be made to the surface radiant flux densities (all radiative balance compoments), where results for different regions, like Southern America, Southern Africa, Northern America, Europe, and Indonesia, will be presented. Applying SESAT, energy flux densities, like latent and sensible heat flux densities could also be determined additionally. A statistical analysis of all results including a detailed discussion for the two spatial resolutions will close this study.

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.

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.

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...

A Fuzzy Expert System for Distinguishing between Bacterial and Aseptic Meningitis

Directory of Open Access Journals (Sweden)

Mostafa Langarizadeh

2015-05-01

Full Text Available Introduction Bacterial meningitis is a known infectious disease which occurs at early ages and should be promptly diagnosed and treated. Bacterial and aseptic meningitis are hard to be distinguished. Therefore, physicians should be highly informed and experienced in this area. The main aim of this study was to suggest a system for distinguishing between bacterial and aseptic meningitis, using fuzzy logic.    Materials and Methods In the first step, proper attributes were selected using Weka 3.6.7 software. Six attributes were selected using Attribute Evaluator, InfoGainAttributeEval, and Ranker search method items. Then, a fuzzy inference engine was designed using MATLAB software, based on Mamdani’s fuzzy logic method with max-min composition, prod-probor, and centroid defuzzification. The rule base consisted of eight rules, based on the experience of three specialists and information extracted from textbooks. Results Data were extracted from 106 records of patients with meningitis (42 cases with bacterial meningitis in order to evaluate the proposed system. The system accuracy, specificity, and sensitivity were 89%, 92 %, and 97%, respectively. The area under the ROC curve was 0.93, and Kappa test revealed a good level of agreement (k=0.84, P

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

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.

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.

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.

Hierarchy-associated semantic-rule inference framework for classifying indoor scenes

Science.gov (United States)

Yu, Dan; Liu, Peng; Ye, Zhipeng; Tang, Xianglong; Zhao, Wei

2016-03-01

Typically, the initial task of classifying indoor scenes is challenging, because the spatial layout and decoration of a scene can vary considerably. Recent efforts at classifying object relationships commonly depend on the results of scene annotation and predefined rules, making classification inflexible. Furthermore, annotation results are easily affected by external factors. Inspired by human cognition, a scene-classification framework was proposed using the empirically based annotation (EBA) and a match-over rule-based (MRB) inference system. The semantic hierarchy of images is exploited by EBA to construct rules empirically for MRB classification. The problem of scene classification is divided into low-level annotation and high-level inference from a macro perspective. Low-level annotation involves detecting the semantic hierarchy and annotating the scene with a deformable-parts model and a bag-of-visual-words model. In high-level inference, hierarchical rules are extracted to train the decision tree for classification. The categories of testing samples are generated from the parts to the whole. Compared with traditional classification strategies, the proposed semantic hierarchy and corresponding rules reduce the effect of a variable background and improve the classification performance. The proposed framework was evaluated on a popular indoor scene dataset, and the experimental results demonstrate its effectiveness.

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

Spatial and seasonal distribution patterns of the ragged-tooth shark ...

African Journals Online (AJOL)

Catches from competitive shore-anglers, inshore boatbased anglers and sightings by spearfishers and divers were used to infer the spatial and seasonal movement patterns of young-of-the-year (2.4m TL) ragged-tooth sharks Carcharias taurus along ...

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...

Spatial diversity of bacterioplankton communities in surface water of northern South China Sea.

Science.gov (United States)

Li, Jialin; Li, Nan; Li, Fuchao; Zou, Tao; Yu, Shuxian; Wang, Yinchu; Qin, Song; Wang, Guangyi

2014-01-01

The South China Sea is one of the largest marginal seas, with relatively frequent passage of eddies and featuring distinct spatial variation in the western tropical Pacific Ocean. Here, we report a phylogenetic study of bacterial community structures in surface seawater of the northern South China Sea (nSCS). Samples collected from 31 sites across large environmental gradients were used to construct clone libraries and yielded 2,443 sequences grouped into 170 OTUs. Phylogenetic analysis revealed 23 bacterial classes with major components α-, β- and γ-Proteobacteria, as well as Cyanobacteria. At class and genus taxon levels, community structure of coastal waters was distinctively different from that of deep-sea waters and displayed a higher diversity index. Redundancy analyses revealed that bacterial community structures displayed a significant correlation with the water depth of individual sampling sites. Members of α-Proteobacteria were the principal component contributing to the differences of the clone libraries. Furthermore, the bacterial communities exhibited heterogeneity within zones of upwelling and anticyclonic eddies. Our results suggested that surface bacterial communities in nSCS had two-level patterns of spatial distribution structured by ecological types (coastal VS. oceanic zones) and mesoscale physical processes, and also provided evidence for bacterial phylogenetic phyla shaped by ecological preferences.

Spatial diversity of bacterioplankton communities in surface water of northern South China Sea.

Directory of Open Access Journals (Sweden)

Jialin Li

Full Text Available The South China Sea is one of the largest marginal seas, with relatively frequent passage of eddies and featuring distinct spatial variation in the western tropical Pacific Ocean. Here, we report a phylogenetic study of bacterial community structures in surface seawater of the northern South China Sea (nSCS. Samples collected from 31 sites across large environmental gradients were used to construct clone libraries and yielded 2,443 sequences grouped into 170 OTUs. Phylogenetic analysis revealed 23 bacterial classes with major components α-, β- and γ-Proteobacteria, as well as Cyanobacteria. At class and genus taxon levels, community structure of coastal waters was distinctively different from that of deep-sea waters and displayed a higher diversity index. Redundancy analyses revealed that bacterial community structures displayed a significant correlation with the water depth of individual sampling sites. Members of α-Proteobacteria were the principal component contributing to the differences of the clone libraries. Furthermore, the bacterial communities exhibited heterogeneity within zones of upwelling and anticyclonic eddies. Our results suggested that surface bacterial communities in nSCS had two-level patterns of spatial distribution structured by ecological types (coastal VS. oceanic zones and mesoscale physical processes, and also provided evidence for bacterial phylogenetic phyla shaped by ecological preferences.

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

Ecological modeling from time-series inference: insight into dynamics and stability of intestinal microbiota.

Directory of Open Access Journals (Sweden)

Richard R Stein

Full Text Available The intestinal microbiota is a microbial ecosystem of crucial importance to human health. Understanding how the microbiota confers resistance against enteric pathogens and how antibiotics disrupt that resistance is key to the prevention and cure of intestinal infections. We present a novel method to infer microbial community ecology directly from time-resolved metagenomics. This method extends generalized Lotka-Volterra dynamics to account for external perturbations. Data from recent experiments on antibiotic-mediated Clostridium difficile infection is analyzed to quantify microbial interactions, commensal-pathogen interactions, and the effect of the antibiotic on the community. Stability analysis reveals that the microbiota is intrinsically stable, explaining how antibiotic perturbations and C. difficile inoculation can produce catastrophic shifts that persist even after removal of the perturbations. Importantly, the analysis suggests a subnetwork of bacterial groups implicated in protection against C. difficile. Due to its generality, our method can be applied to any high-resolution ecological time-series data to infer community structure and response to external stimuli.

Ecological modeling from time-series inference: insight into dynamics and stability of intestinal microbiota.

Science.gov (United States)

Stein, Richard R; Bucci, Vanni; Toussaint, Nora C; Buffie, Charlie G; Rätsch, Gunnar; Pamer, Eric G; Sander, Chris; Xavier, João B

2013-01-01

The intestinal microbiota is a microbial ecosystem of crucial importance to human health. Understanding how the microbiota confers resistance against enteric pathogens and how antibiotics disrupt that resistance is key to the prevention and cure of intestinal infections. We present a novel method to infer microbial community ecology directly from time-resolved metagenomics. This method extends generalized Lotka-Volterra dynamics to account for external perturbations. Data from recent experiments on antibiotic-mediated Clostridium difficile infection is analyzed to quantify microbial interactions, commensal-pathogen interactions, and the effect of the antibiotic on the community. Stability analysis reveals that the microbiota is intrinsically stable, explaining how antibiotic perturbations and C. difficile inoculation can produce catastrophic shifts that persist even after removal of the perturbations. Importantly, the analysis suggests a subnetwork of bacterial groups implicated in protection against C. difficile. Due to its generality, our method can be applied to any high-resolution ecological time-series data to infer community structure and response to external stimuli.

Real-time detection of antibiotic activity by measuring nanometer-scale bacterial deformation

Science.gov (United States)

Iriya, Rafael; Syal, Karan; Jing, Wenwen; Mo, Manni; Yu, Hui; Haydel, Shelley E.; Wang, Shaopeng; Tao, Nongjian

2017-12-01

Diagnosing antibiotic-resistant bacteria currently requires sensitive detection of phenotypic changes associated with antibiotic action on bacteria. Here, we present an optical imaging-based approach to quantify bacterial membrane deformation as a phenotypic feature in real-time with a nanometer scale (˜9 nm) detection limit. Using this approach, we found two types of antibiotic-induced membrane deformations in different bacterial strains: polymyxin B induced relatively uniform spatial deformation of Escherichia coli O157:H7 cells leading to change in cellular volume and ampicillin-induced localized spatial deformation leading to the formation of bulges or protrusions on uropathogenic E. coli CFT073 cells. We anticipate that the approach will contribute to understanding of antibiotic phenotypic effects on bacteria with a potential for applications in rapid antibiotic susceptibility testing.

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.

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

Models of deletion for visualizing bacterial variation: an application to tuberculosis spoligotypes

Directory of Open Access Journals (Sweden)

Francis Andrew R

2008-11-01

Full Text Available Abstract Background Molecular typing methods are commonly used to study genetic relationships among bacterial isolates. Many of these methods have become standardized and produce portable data. A popular approach for analyzing such data is to construct graphs, including phylogenies. Inferences from graph representations of data assist in understanding the patterns of transmission of bacterial pathogens, and basing these graph constructs on biological models of evolution of the molecular marker helps make these inferences. Spoligotyping is a widely used method for genotyping isolates of Mycobacterium tuberculosis that exploits polymorphism in the direct repeat region. Our goal was to examine a range of models describing the evolution of spoligotypes in order to develop a visualization method to represent likely relationships among M. tuberculosis isolates. Results We found that inferred mutations of spoligotypes frequently involve the loss of a single or very few adjacent spacers. Using a second-order variant of Akaike's Information Criterion, we selected the Zipf model as the basis for resolving ambiguities in the ancestry of spoligotypes. We developed a method to construct graphs of spoligotypes (which we call spoligoforests. To demonstrate this method, we applied it to a tuberculosis data set from Cuba and compared the method to some existing methods. Conclusion We propose a new approach in analyzing relationships of M. tuberculosis isolates using spoligotypes. The spoligoforest recovers a plausible history of transmission and mutation events based on the selected deletion model. The method may be suitable to study markers based on loci of similar structure from other bacteria. The groupings and relationships in the spoligoforest can be analyzed along with the clinical features of strains to provide an understanding of the evolution of spoligotypes.

Stair-Step Pattern of Soil Bacterial Diversity Mainly Driven by pH and Vegetation Types Along the Elevational Gradients of Gongga Mountain, China.

Science.gov (United States)

Li, Jiabao; Shen, Zehao; Li, Chaonan; Kou, Yongping; Wang, Yansu; Tu, Bo; Zhang, Shiheng; Li, Xiangzhen

2018-01-01

Ecological understandings of soil bacterial community succession and assembly mechanism along elevational gradients in mountains remain not well understood. Here, by employing the high-throughput sequencing technique, we systematically examined soil bacterial diversity patterns, the driving factors, and community assembly mechanisms along the elevational gradients of 1800-4100 m on Gongga Mountain in China. Soil bacterial diversity showed an extraordinary stair-step pattern along the elevational gradients. There was an abrupt decrease of bacterial diversity between 2600 and 2800 m, while no significant change at either lower (1800-2600 m) or higher (2800-4100 m) elevations, which coincided with the variation in soil pH. In addition, the community structure differed significantly between the lower and higher elevations, which could be primarily attributed to shifts in soil pH and vegetation types. Although there was no direct effect of MAP and MAT on bacterial community structure, our partial least squares path modeling analysis indicated that bacterial communities were indirectly influenced by climate via the effect on vegetation and the derived effect on soil properties. As for bacterial community assembly mechanisms, the null model analysis suggested that environmental filtering played an overwhelming role in the assembly of bacterial communities in this region. In addition, variation partition analysis indicated that, at lower elevations, environmental attributes explained much larger fraction of the β-deviation than spatial attributes, while spatial attributes increased their contributions at higher elevations. Our results highlight the importance of environmental filtering, as well as elevation-related spatial attributes in structuring soil bacterial communities in mountain ecosystems.

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.

Spatiotemporal variation of bacterial community composition and possible controlling factors in tropical shallow lagoons.

Science.gov (United States)

Laque, Thaís; Farjalla, Vinicius F; Rosado, Alexandre S; Esteves, Francisco A

2010-05-01

Bacterial community composition (BCC) has been extensively related to specific environmental conditions. Tropical coastal lagoons present great temporal and spatial variation in their limnological conditions, which, in turn, should influence the BCC. Here, we sought for the limnological factors that influence, in space and time, the BCC in tropical coastal lagoons (Rio de Janeiro State, Brazil). The Visgueiro lagoon was sampled monthly for 1 year and eight lagoons were sampled once for temporal and spatial analysis, respectively. BCC was evaluated by bacteria-specific PCR-DGGE methods. Great variations were observed in limnological conditions and BCC on both temporal and spatial scales. Changes in the BCC of Visgueiro lagoon throughout the year were best related to salinity and concentrations of NO (3) (-) , dissolved phosphorus and chlorophyll-a, while changes in BCC between lagoons were best related to salinity and dissolved phosphorus concentration. Salinity has a direct impact on the integrity of the bacterial cell, and it was previously observed that phosphorus is the main limiting nutrient to bacterial growth in these lagoons. Therefore, we conclude that great variations in limnological conditions of coastal lagoons throughout time and space resulted in different BCCs and salinity and nutrient concentration, particularly dissolved phosphorus, are the main limnological factors influencing BCC in these tropical coastal lagoons.

Exploring the effects of photon correlations from thermal sources on bacterial photosynthesis

OpenAIRE

Manrique, Pedro D.; Caycedo-Soler, Felipe; De Mendoza, Adriana; Rodríguez, Ferney; Quiroga, Luis; Johnson, Neil F.

2016-01-01

Thermal light sources can produce photons with strong spatial correlations. We study the role that these correlations might potentially play in bacterial photosynthesis. Our findings show a relationship between the transversal distance between consecutive absorptions and the efficiency of the photosynthetic process. Furthermore, membranes where the clustering of core complexes (so-called RC-LH1) is high, display a range where the organism profits maximally from the spatial correlation of the ...

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.

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.

Associations between soil bacterial community structure and nutrient cycling functions in long-term organic farm soils following cover crop and organic fertilizer amendment.

Science.gov (United States)

Fernandez, Adria L; Sheaffer, Craig C; Wyse, Donald L; Staley, Christopher; Gould, Trevor J; Sadowsky, Michael J

2016-10-01

Agricultural management practices can produce changes in soil microbial populations whose functions are crucial to crop production and may be detectable using high-throughput sequencing of bacterial 16S rRNA. To apply sequencing-derived bacterial community structure data to on-farm decision-making will require a better understanding of the complex associations between soil microbial community structure and soil function. Here 16S rRNA sequencing was used to profile soil bacterial communities following application of cover crops and organic fertilizer treatments in certified organic field cropping systems. Amendment treatments were hairy vetch (Vicia villosa), winter rye (Secale cereale), oilseed radish (Raphanus sativus), buckwheat (Fagopyrum esculentum), beef manure, pelleted poultry manure, Sustane(®) 8-2-4, and a no-amendment control. Enzyme activities, net N mineralization, soil respiration, and soil physicochemical properties including nutrient levels, organic matter (OM) and pH were measured. Relationships between these functional and physicochemical parameters and soil bacterial community structure were assessed using multivariate methods including redundancy analysis, discriminant analysis, and Bayesian inference. Several cover crops and fertilizers affected soil functions including N-acetyl-β-d-glucosaminidase and β-glucosidase activity. Effects, however, were not consistent across locations and sampling timepoints. Correlations were observed among functional parameters and relative abundances of individual bacterial families and phyla. Bayesian analysis inferred no directional relationships between functional activities, bacterial families, and physicochemical parameters. Soil functional profiles were more strongly predicted by location than by treatment, and differences were largely explained by soil physicochemical parameters. Composition of soil bacterial communities was predictive of soil functional profiles. Differences in soil function were

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.

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

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...

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.

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 ...

Bayesian Inference of Forces Causing Cytoplasmic Streaming in Caenorhabditis elegans Embryos and Mouse Oocytes.

Science.gov (United States)

Niwayama, Ritsuya; Nagao, Hiromichi; Kitajima, Tomoya S; Hufnagel, Lars; Shinohara, Kyosuke; Higuchi, Tomoyuki; Ishikawa, Takuji; Kimura, Akatsuki

2016-01-01

Cellular structures are hydrodynamically interconnected, such that force generation in one location can move distal structures. One example of this phenomenon is cytoplasmic streaming, whereby active forces at the cell cortex induce streaming of the entire cytoplasm. However, it is not known how the spatial distribution and magnitude of these forces move distant objects within the cell. To address this issue, we developed a computational method that used cytoplasm hydrodynamics to infer the spatial distribution of shear stress at the cell cortex induced by active force generators from experimentally obtained flow field of cytoplasmic streaming. By applying this method, we determined the shear-stress distribution that quantitatively reproduces in vivo flow fields in Caenorhabditis elegans embryos and mouse oocytes during meiosis II. Shear stress in mouse oocytes were predicted to localize to a narrower cortical region than that with a high cortical flow velocity and corresponded with the localization of the cortical actin cap. The predicted patterns of pressure gradient in both species were consistent with species-specific cytoplasmic streaming functions. The shear-stress distribution inferred by our method can contribute to the characterization of active force generation driving biological streaming.

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.

The role of right frontal brain regions in integration of spatial relation.

Science.gov (United States)

Han, Jiahui; Cao, Bihua; Cao, Yunfei; Gao, Heming; Li, Fuhong

2016-06-01

Previous studies have explored the neural mechanisms of spatial reasoning on a two-dimensional (2D) plane; however, it remains unclear how spatial reasoning is conducted in a three-dimensional (3D) condition. In the present study, we presented 3D geometric objects to 16 adult participants, and asked them to process the spatial relationship between different corners of the geometric objects. In premise-1, the first two corners of a geometric shape (e.g., A vs. B) were displayed. In premise-2, the second and third corners (e.g., B vs. C) were displayed. After integrating the two premises, participants were required to infer the spatial relationship between the first and the third corners (e.g., A and C). Finally, the participants were presented with a conclusion object, and they were required to judge whether the conclusion was true or false based on their inference. The event-related potential evoked by premise-2 revealed that (1) compared with 2D spatial reasoning, 3D reasoning elicited a smaller P3b component, and (2) in the right frontal areas, increased negativities were found in the 3D condition during the N400 and late negative components (LNC). These findings imply that higher brain activity in the right frontal brain regions were related with the integration and maintenance of spatial information in working memory for reasoning. Copyright © 2016 Elsevier Ltd. All rights reserved.

BSim: an agent-based tool for modeling bacterial populations in systems and synthetic biology.

Directory of Open Access Journals (Sweden)

Thomas E Gorochowski

Full Text Available Large-scale collective behaviors such as synchronization and coordination spontaneously arise in many bacterial populations. With systems biology attempting to understand these phenomena, and synthetic biology opening up the possibility of engineering them for our own benefit, there is growing interest in how bacterial populations are best modeled. Here we introduce BSim, a highly flexible agent-based computational tool for analyzing the relationships between single-cell dynamics and population level features. BSim includes reference implementations of many bacterial traits to enable the quick development of new models partially built from existing ones. Unlike existing modeling tools, BSim fully considers spatial aspects of a model allowing for the description of intricate micro-scale structures, enabling the modeling of bacterial behavior in more realistic three-dimensional, complex environments. The new opportunities that BSim opens are illustrated through several diverse examples covering: spatial multicellular computing, modeling complex environments, population dynamics of the lac operon, and the synchronization of genetic oscillators. BSim is open source software that is freely available from http://bsim-bccs.sf.net and distributed under the Open Source Initiative (OSI recognized MIT license. Developer documentation and a wide range of example simulations are also available from the website. BSim requires Java version 1.6 or higher.

Contrasting diversity patterns of crenarchaeal, bacterial and fungal soil communities in an alpine landscape.

Directory of Open Access Journals (Sweden)

Lucie Zinger

2011-05-01

Full Text Available The advent of molecular techniques in microbial ecology has aroused interest in gaining an understanding about the spatial distribution of regional pools of soil microbes and the main drivers responsible of these spatial patterns. Here, we assessed the distribution of crenarcheal, bacterial and fungal communities in an alpine landscape displaying high turnover in plant species over short distances. Our aim is to determine the relative contribution of plant species composition, environmental conditions, and geographic isolation on microbial community distribution.Eleven types of habitats that best represent the landscape heterogeneity were investigated. Crenarchaeal, bacterial and fungal communities were described by means of Single Strand Conformation Polymorphism. Relationships between microbial beta diversity patterns were examined by using Bray-Curtis dissimilarities and Principal Coordinate Analyses. Distance-based redundancy analyses and variation partitioning were used to estimate the relative contributions of different drivers on microbial beta diversity. Microbial communities tended to be habitat-specific and did not display significant spatial autocorrelation. Microbial beta diversity correlated with soil pH. Fungal beta-diversity was mainly related to soil organic matter. Though the effect of plant species composition was significant for all microbial groups, it was much stronger for Fungi. In contrast, geographic distances did not have any effect on microbial beta diversity.Microbial communities exhibit non-random spatial patterns of diversity in alpine landscapes. Crenarcheal, bacterial and fungal community turnover is high and associated with plant species composition through different set of soil variables, but is not caused by geographical isolation.

Continuous time modelling of dynamical spatial lattice data observed at sparsely distributed times

DEFF Research Database (Denmark)

Rasmussen, Jakob Gulddahl; Møller, Jesper

2007-01-01

Summary. We consider statistical and computational aspects of simulation-based Bayesian inference for a spatial-temporal model based on a multivariate point process which is only observed at sparsely distributed times. The point processes are indexed by the sites of a spatial lattice......, and they exhibit spatial interaction. For specificity we consider a particular dynamical spatial lattice data set which has previously been analysed by a discrete time model involving unknown normalizing constants. We discuss the advantages and disadvantages of using continuous time processes compared...... with discrete time processes in the setting of the present paper as well as other spatial-temporal situations....

Gaussian Process Regression Model in Spatial Logistic Regression

Science.gov (United States)

Sofro, A.; Oktaviarina, A.

2018-01-01

Spatial analysis has developed very quickly in the last decade. One of the favorite approaches is based on the neighbourhood of the region. Unfortunately, there are some limitations such as difficulty in prediction. Therefore, we offer Gaussian process regression (GPR) to accommodate the issue. In this paper, we will focus on spatial modeling with GPR for binomial data with logit link function. The performance of the model will be investigated. We will discuss the inference of how to estimate the parameters and hyper-parameters and to predict as well. Furthermore, simulation studies will be explained in the last section.

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.

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.

Bayesian inference for multivariate point processes observed at sparsely distributed times

DEFF Research Database (Denmark)

Rasmussen, Jakob Gulddahl; Møller, Jesper; Aukema, B.H.

We consider statistical and computational aspects of simulation-based Bayesian inference for a multivariate point process which is only observed at sparsely distributed times. For specicity we consider a particular data set which has earlier been analyzed by a discrete time model involving unknown...... normalizing constants. We discuss the advantages and disadvantages of using continuous time processes compared to discrete time processes in the setting of the present paper as well as other spatial-temporal situations. Keywords: Bark beetle, conditional intensity, forest entomology, Markov chain Monte Carlo...

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.

Prokaryotes in subsoil – evidence for spatial separation of oligotrophs and copiotrophs by co-occurrence networks

Directory of Open Access Journals (Sweden)

Michael eSchloter

2015-11-01

Full Text Available Soil microbial communities provide a wide range of soil functions including nutrient cycling, soil formation, and plant growth promotion. On the small scale, nutrient rich soil hotspots developed from soil animal or plant activity are important drivers for microbial communities and their activity pattern. Nevertheless, in subsoil, the spatial heterogeneity of microbes with diverging lifestyles has been barely considered so far. In this study, the phylogenetic composition of the bacterial and archaeal microbiome based on 16S rRNA gene pyrosequencing was investigated in the soil compartments bulk soil, drilosphere, and rhizosphere in topsoil and in the subsoil of an agricultural field. With co-occurrence network analysis, the spatial separation of typically oligotrophs and heterotrophs in subsoil and hotspots was assessed. Four co-occurring bacterial communities were identified and attributed to bulk topsoil, bulk subsoil, drilosphere, and rhizosphere. The bacterial phyla Proteobacteria and Bacteroidetes, which represent many copiotrophic bacteria, are affiliated to the hotspot communities – the rhizosphere and drilosphere – both in topsoil and subsoil. Acidobacteria, Actinobacteria, Gemmatimonadetes, Planctomycetes, and Verrucomicrobia with many oligotrophic bacteria, are the abundant groups of the bulk subsoil community. The bacterial core microbiome in this soil was estimated and only covers 7.6% of the bacterial sequencing reads but includes both oligotrophic and copiotrophic bacteria. Instead, the archaeal core microbiome includes 56% of the overall archaeal diversity and comprises only the ammonium oxidizing Nitrososphaera. Thus, the spatial variability of nutrient quality and quantity strongly shapes the bacterial community composition and their interaction in subsoil, whereas archaea are a stable backbone of the soil prokaryotes.

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.

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

Causation in risk assessment and management: models, inference, biases, and a microbial risk-benefit case study.

Science.gov (United States)

Cox, L A; Ricci, P F

2005-04-01

Causal inference of exposure-response relations from data is a challenging aspect of risk assessment with important implications for public and private risk management. Such inference, which is fundamentally empirical and based on exposure (or dose)-response models, seldom arises from a single set of data; rather, it requires integrating heterogeneous information from diverse sources and disciplines including epidemiology, toxicology, and cell and molecular biology. The causal aspects we discuss focus on these three aspects: drawing sound inferences about causal relations from one or more observational studies; addressing and resolving biases that can affect a single multivariate empirical exposure-response study; and applying the results from these considerations to the microbiological risk management of human health risks and benefits of a ban on antibiotic use in animals, in the context of banning enrofloxacin or macrolides, antibiotics used against bacterial illnesses in poultry, and the effects of such bans on changing the risk of human food-borne campylobacteriosis infections. The purposes of this paper are to describe novel causal methods for assessing empirical causation and inference; exemplify how to deal with biases that routinely arise in multivariate exposure- or dose-response modeling; and provide a simplified discussion of a case study of causal inference using microbial risk analysis as an example. The case study supports the conclusion that the human health benefits from a ban are unlikely to be greater than the excess human health risks that it could create, even when accounting for uncertainty. We conclude that quantitative causal analysis of risks is a preferable to qualitative assessments because it does not involve unjustified loss of information and is sound under the inferential use of risk results by management.

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

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.

Spatial capture-recapture: a promising method for analyzing data collected using artificial cover objects

Science.gov (United States)

Sutherland, Chris; Munoz, David; Miller, David A.W.; Grant, Evan H. Campbell

2016-01-01

Spatial capture–recapture (SCR) is a relatively recent development in ecological statistics that provides a spatial context for estimating abundance and space use patterns, and improves inference about absolute population density. SCR has been applied to individual encounter data collected noninvasively using methods such as camera traps, hair snares, and scat surveys. Despite the widespread use of capture-based surveys to monitor amphibians and reptiles, there are few applications of SCR in the herpetological literature. We demonstrate the utility of the application of SCR for studies of reptiles and amphibians by analyzing capture–recapture data from Red-Backed Salamanders, Plethodon cinereus, collected using artificial cover boards. Using SCR to analyze spatial encounter histories of marked individuals, we found evidence that density differed little among four sites within the same forest (on average, 1.59 salamanders/m2) and that salamander detection probability peaked in early October (Julian day 278) reflecting expected surface activity patterns of the species. The spatial scale of detectability, a measure of space use, indicates that the home range size for this population of Red-Backed Salamanders in autumn was 16.89 m2. Surveying reptiles and amphibians using artificial cover boards regularly generates spatial encounter history data of known individuals, which can readily be analyzed using SCR methods, providing estimates of absolute density and inference about the spatial scale of habitat use.

Nematode grazing promotes bacterial community dynamics in soil at the aggregate level.

Science.gov (United States)

Jiang, Yuji; Liu, Manqiang; Zhang, Jiabao; Chen, Yan; Chen, Xiaoyun; Chen, Lijun; Li, Huixin; Zhang, Xue-Xian; Sun, Bo

2017-12-01

Nematode predation has important roles in determining bacterial community composition and dynamics, but the extent of the effects remains largely rudimentary, particularly in natural environment settings. Here, we investigated the complex microbial-microfaunal interactions in the rhizosphere of maize grown in red soils, which were derived from four long-term fertilization regimes. Root-free rhizosphere soil samples were separated into three aggregate fractions whereby the abundance and community composition were examined for nematode and total bacterial communities. A functional group of alkaline phosphomonoesterase (ALP) producing bacteria was included to test the hypothesis that nematode grazing may significantly affect specific bacteria-mediated ecological functions, that is, organic phosphate cycling in soil. Results of correlation analysis, structural equation modeling and interaction networks combined with laboratory microcosm experiments consistently indicated that bacterivorous nematodes enhanced bacterial diversity, and the abundance of bacterivores was positively correlated with bacterial biomass, including ALP-producing bacterial abundance. Significantly, such effects were more pronounced in large macroaggregates than in microaggregates. There was a positive correlation between the most dominant bacterivores Protorhabditis and the ALP-producing keystone 'species' Mesorhizobium. Taken together, these findings implicate important roles of nematodes in stimulating bacterial dynamics in a spatially dependent manner.

MobilomeFINDER: web-based tools for in silico and experimental discovery of bacterial genomic islands

OpenAIRE

Ou, Hong-Yu; He, Xinyi; Harrison, Ewan M.; Kulasekara, Bridget R.; Thani, Ali Bin; Kadioglu, Aras; Lory, Stephen; Hinton, Jay C. D.; Barer, Michael R.; Deng, Zixin; Rajakumar, Kumar

2007-01-01

MobilomeFINDER (http://mml.sjtu.edu.cn/MobilomeFINDER) is an interactive online tool that facilitates bacterial genomic island or ‘mobile genome’ (mobilome) discovery; it integrates the ArrayOme and tRNAcc software packages. ArrayOme utilizes a microarray-derived comparative genomic hybridization input data set to generate ‘inferred contigs’ produced by merging adjacent genes classified as ‘present’. Collectively these ‘fragments’ represent a hypothetical ‘microarray-visualized genome (MVG)’....

Gaussian likelihood inference on data from trans-Gaussian random fields with Matérn covariance function

KAUST Repository

Yan, Yuan; Genton, Marc G.

2017-01-01

Gaussian likelihood inference has been studied and used extensively in both statistical theory and applications due to its simplicity. However, in practice, the assumption of Gaussianity is rarely met in the analysis of spatial data. In this paper, we study the effect of non-Gaussianity on Gaussian likelihood inference for the parameters of the Matérn covariance model. By using Monte Carlo simulations, we generate spatial data from a Tukey g-and-h random field, a flexible trans-Gaussian random field, with the Matérn covariance function, where g controls skewness and h controls tail heaviness. We use maximum likelihood based on the multivariate Gaussian distribution to estimate the parameters of the Matérn covariance function. We illustrate the effects of non-Gaussianity of the data on the estimated covariance function by means of functional boxplots. Thanks to our tailored simulation design, a comparison of the maximum likelihood estimator under both the increasing and fixed domain asymptotics for spatial data is performed. We find that the maximum likelihood estimator based on Gaussian likelihood is overall satisfying and preferable than the non-distribution-based weighted least squares estimator for data from the Tukey g-and-h random field. We also present the result for Gaussian kriging based on Matérn covariance estimates with data from the Tukey g-and-h random field and observe an overall satisfactory performance.

Gaussian likelihood inference on data from trans-Gaussian random fields with Matérn covariance function

KAUST Repository

Yan, Yuan

2017-07-13

Gaussian likelihood inference has been studied and used extensively in both statistical theory and applications due to its simplicity. However, in practice, the assumption of Gaussianity is rarely met in the analysis of spatial data. In this paper, we study the effect of non-Gaussianity on Gaussian likelihood inference for the parameters of the Matérn covariance model. By using Monte Carlo simulations, we generate spatial data from a Tukey g-and-h random field, a flexible trans-Gaussian random field, with the Matérn covariance function, where g controls skewness and h controls tail heaviness. We use maximum likelihood based on the multivariate Gaussian distribution to estimate the parameters of the Matérn covariance function. We illustrate the effects of non-Gaussianity of the data on the estimated covariance function by means of functional boxplots. Thanks to our tailored simulation design, a comparison of the maximum likelihood estimator under both the increasing and fixed domain asymptotics for spatial data is performed. We find that the maximum likelihood estimator based on Gaussian likelihood is overall satisfying and preferable than the non-distribution-based weighted least squares estimator for data from the Tukey g-and-h random field. We also present the result for Gaussian kriging based on Matérn covariance estimates with data from the Tukey g-and-h random field and observe an overall satisfactory performance.

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

Science.gov (United States)

Huang, Yi-Fei; Golding, G Brian

2015-02-15

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

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...

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

Spatial occupancy models applied to atlas data show Southern Ground Hornbills strongly depend on protected areas.

Science.gov (United States)

Broms, Kristin M; Johnson, Devin S; Altwegg, Res; Conquest, Loveday L

2014-03-01

Determining the range of a species and exploring species--habitat associations are central questions in ecology and can be answered by analyzing presence--absence data. Often, both the sampling of sites and the desired area of inference involve neighboring sites; thus, positive spatial autocorrelation between these sites is expected. Using survey data for the Southern Ground Hornbill (Bucorvus leadbeateri) from the Southern African Bird Atlas Project, we compared advantages and disadvantages of three increasingly complex models for species occupancy: an occupancy model that accounted for nondetection but assumed all sites were independent, and two spatial occupancy models that accounted for both nondetection and spatial autocorrelation. We modeled the spatial autocorrelation with an intrinsic conditional autoregressive (ICAR) model and with a restricted spatial regression (RSR) model. Both spatial models can readily be applied to any other gridded, presence--absence data set using a newly introduced R package. The RSR model provided the best inference and was able to capture small-scale variation that the other models did not. It showed that ground hornbills are strongly dependent on protected areas in the north of their South African range, but less so further south. The ICAR models did not capture any spatial autocorrelation in the data, and they took an order, of magnitude longer than the RSR models to run. Thus, the RSR occupancy model appears to be an attractive choice for modeling occurrences at large spatial domains, while accounting for imperfect detection and spatial autocorrelation.

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

NIFTY - Numerical Information Field Theory. A versatile PYTHON library for signal inference

Science.gov (United States)

Selig, M.; Bell, M. R.; Junklewitz, H.; Oppermann, N.; Reinecke, M.; Greiner, M.; Pachajoa, C.; Enßlin, T. A.

2013-06-01

NIFTy (Numerical Information Field Theory) is a software package designed to enable the development of signal inference algorithms that operate regardless of the underlying spatial grid and its resolution. Its object-oriented framework is written in Python, although it accesses libraries written in Cython, C++, and C for efficiency. NIFTy offers a toolkit that abstracts discretized representations of continuous spaces, fields in these spaces, and operators acting on fields into classes. Thereby, the correct normalization of operations on fields is taken care of automatically without concerning the user. This allows for an abstract formulation and programming of inference algorithms, including those derived within information field theory. Thus, NIFTy permits its user to rapidly prototype algorithms in 1D, and then apply the developed code in higher-dimensional settings of real world problems. The set of spaces on which NIFTy operates comprises point sets, n-dimensional regular grids, spherical spaces, their harmonic counterparts, and product spaces constructed as combinations of those. The functionality and diversity of the package is demonstrated by a Wiener filter code example that successfully runs without modification regardless of the space on which the inference problem is defined. NIFTy homepage http://www.mpa-garching.mpg.de/ift/nifty/; Excerpts of this paper are part of the NIFTy source code and documentation.

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

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...

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...

The Use of Spatial Cognition in Graph Interpretation

Science.gov (United States)

2007-08-01

Mathematics has emphasized the importance of proactively teaching students of all ages to interpret graphs and use them to make inferences ( NCTM ... Mathematics . Reston, VA: National Council of Teachers of Mathematics . Oh, S., & Kim, M. (2004). The role of spatial working memory in visual...in learning science (Schunn et al, in press). Not coincidentally, in developing its recent national standards, the National Council of Teachers of

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

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.

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.

A Multi-Element Approach to Location Inference of Twitter: A Case for Emergency Response

Directory of Open Access Journals (Sweden)

Farhad Laylavi

2016-04-01

Full Text Available Since its inception, Twitter has played a major role in real-world events—especially in the aftermath of disasters and catastrophic incidents, and has been increasingly becoming the first point of contact for users wishing to provide or seek information about such situations. The use of Twitter in emergency response and disaster management opens up avenues of research concerning different aspects of Twitter data quality, usefulness and credibility. A real challenge that has attracted substantial attention in the Twitter research community exists in the location inference of twitter data. Considering that less than 2% of tweets are geotagged, finding location inference methods that can go beyond the geotagging capability is undoubtedly the priority research area. This is especially true in terms of emergency response, where spatial aspects of information play an important role. This paper introduces a multi-elemental location inference method that puts the geotagging aside and tries to predict the location of tweets by exploiting the other inherently attached data elements. In this regard, textual content, users’ profile location and place labelling, as the main location-related elements, are taken into account. Location-name classes in three granularity levels are defined and employed to look up the location references from the location-associated elements. The inferred location of the finest granular level is assigned to a tweet, based on a novel location assignment rule. The location assigned by the location inference process is considered to be the inferred location of a tweet, and is compared with the geotagged coordinates as the ground truth of the study. The results show that this method is able to successfully infer the location of 87% of the tweets at the average distance error of 12.2 km and the median distance error of 4.5 km, which is a significant improvement compared with that of the current methods that can predict the location

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 ...

Effect of flow and active mixing on bacterial growth in a colon-like geometry

Science.gov (United States)

Cremer, Jonas; Segota, Igor; Arnoldini, Markus; Groisman, Alex; Hwa, Terence

The large intestine harbors bacteria from hundreds of species, with bacterial densities reaching up to 1012 cells per gram. Many different factors influence bacterial growth dynamics and thus bacterial density and microbiota composition. One dominant force is flow which can in principle lead to a washout of bacteria from the proximal colon. Active mixing by Contractions of the colonic wall together with bacterial growth might counteract such flow-forces and allow high bacterial densities to occur. As a step towards understanding bacterial growth in the presence of mixing and flow, we constructed an in-vitro setup where controlled wall-deformations of a channel emulate Contractions. We investigate growth along the channel under a steady nutrient inflow. In the limits of no or very frequent Contractions, the device behaves like a plug-flow reactor and a chemostat respectively. Depending on mixing and flow, we observe varying spatial gradients in bacterial density along the channel. Active mixing by deformations of the channel wall is shown to be crucial in maintaining a steady-state bacterial population in the presence of flow. The growth-dynamics is quantitatively captured by a simple mathematical model, with the effect of mixing described by an effective diffusion term.

Bacterial cooperation in the wild and in the clinic: are pathogen social behaviours relevant outside the laboratory?

Science.gov (United States)

Harrison, Freya

2013-02-01

Individual bacterial cells can communicate via quorum sensing, cooperate to harvest nutrients from their environment, form multicellular biofilms, compete over resources and even kill one another. When the environment that bacteria inhabit is an animal host, these social behaviours mediate virulence. Over the last decade, much attention has focussed on the ecology, evolution and pathology of bacterial cooperation, and the possibility that it could be exploited or destabilised to treat infections. But how far can we really extrapolate from theoretical predictions and laboratory experiments to make inferences about 'cooperative' behaviours in hosts and reservoirs? To determine the likely importance and evolution of cooperation 'in the wild', several questions must be addressed. A recent paper that reports the dynamics of bacterial cooperation and virulence in a field experiment provides an excellent nucleus for bringing together key empirical and theoretical results which help us to frame - if not completely to answer - these questions. Copyright © 2013 WILEY Periodicals, Inc.

Doing ecohydrology backward: Inferring wetland flow and hydroperiod from landscape patterns

Science.gov (United States)

Acharya, Subodh; Kaplan, David A.; Jawitz, James W.; Cohen, Matthew J.

2017-07-01

Human alterations to hydrology have globally impacted wetland ecosystems. Preventing or reversing these impacts is a principal focus of restoration efforts. However, restoration effectiveness is often hampered by limited information on historical landscape properties and hydrologic regime. To help address this gap, we developed a novel statistical approach for inferring flows and inundation frequency (i.e., hydroperiod, HP) in wetlands where changes in spatial vegetation and geomorphic patterns have occurred due to hydrologic alteration. We developed an analytical expression for HP as a transformation of the landscape-scale stage-discharge relationship. We applied this model to the Everglades "ridge-slough" (RS) landscape, a patterned, lotic peatland in southern Florida that has been drastically degraded by compartmentalization, drainage, and flow diversions. The new method reliably estimated flow and HP for a range of RS landscape patterns. Crucially, ridge-patch anisotropy and elevation above sloughs were strong drivers of flow-HP relationships. Increasing ridge heights markedly increased flow required to achieve sufficient HP to support peat accretion. Indeed, ridge heights inferred from historical accounts would require boundary flows 3-4 times greater than today, which agrees with restoration flow estimates from more complex, spatially distributed models. While observed loss of patch anisotropy allows HP targets to be met with lower flows, such landscapes likely fail to support other ecological functions. This work helps inform restoration flows required to restore stable ridge-slough patterning and positive peat accretion in this degraded ecosystem, and, more broadly, provides tools for exploring interactions between landscape and hydrology in lotic wetlands and floodplains.

Harnessing cell-to-cell variations to probe bacterial structure and biophysics

Science.gov (United States)

Cass, Julie A.

Advances in microscopy and biotechnology have given us novel insights into cellular biology and physics. While bacteria were long considered to be relatively unstructured, the development of fluorescence microscopy techniques, and spatially and temporally resolved high-throughput quantitative studies, have uncovered that the bacterial cell is highly organized, and its structure rigorously maintained. In this thesis I will describe our gateTool software, designed to harness cell-to-cell variations to probe bacterial structure, and discuss two exciting aspects of structure that we have employed gateTool to investigate: (i) chromosome organization and the cellular mechanisms for controlling DNA dynamics, and (ii) the study of cell wall synthesis, and how the genes in the synthesis pathway impact cellular shape. In the first project, we develop a spatial and temporal mapping of cell-cycle-dependent chromosomal organization, and use this quantitative map to discover that chromosomal loci segregate from midcell with universal dynamics. In the second project, I describe preliminary time- lapse and snapshot imaging analysis suggesting phentoypical coherence across peptidoglycan synthesis pathways.

Causal Inference for Cross-Modal Action Selection: A Computational Study in a Decision Making Framework.

Science.gov (United States)

Daemi, Mehdi; Harris, Laurence R; Crawford, J Douglas

2016-01-01

Animals try to make sense of sensory information from multiple modalities by categorizing them into perceptions of individual or multiple external objects or internal concepts. For example, the brain constructs sensory, spatial representations of the locations of visual and auditory stimuli in the visual and auditory cortices based on retinal and cochlear stimulations. Currently, it is not known how the brain compares the temporal and spatial features of these sensory representations to decide whether they originate from the same or separate sources in space. Here, we propose a computational model of how the brain might solve such a task. We reduce the visual and auditory information to time-varying, finite-dimensional signals. We introduce controlled, leaky integrators as working memory that retains the sensory information for the limited time-course of task implementation. We propose our model within an evidence-based, decision-making framework, where the alternative plan units are saliency maps of space. A spatiotemporal similarity measure, computed directly from the unimodal signals, is suggested as the criterion to infer common or separate causes. We provide simulations that (1) validate our model against behavioral, experimental results in tasks where the participants were asked to report common or separate causes for cross-modal stimuli presented with arbitrary spatial and temporal disparities. (2) Predict the behavior in novel experiments where stimuli have different combinations of spatial, temporal, and reliability features. (3) Illustrate the dynamics of the proposed internal system. These results confirm our spatiotemporal similarity measure as a viable criterion for causal inference, and our decision-making framework as a viable mechanism for target selection, which may be used by the brain in cross-modal situations. Further, we suggest that a similar approach can be extended to other cognitive problems where working memory is a limiting factor, such

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.

From learning taxonomies to phylogenetic learning: Integration of 16S rRNA gene data into FAME-based bacterial classification

Science.gov (United States)

2010-01-01

Background Machine learning techniques have shown to improve bacterial species classification based on fatty acid methyl ester (FAME) data. Nonetheless, FAME analysis has a limited resolution for discrimination of bacteria at the species level. In this paper, we approach the species classification problem from a taxonomic point of view. Such a taxonomy or tree is typically obtained by applying clustering algorithms on FAME data or on 16S rRNA gene data. The knowledge gained from the tree can then be used to evaluate FAME-based classifiers, resulting in a novel framework for bacterial species classification. Results In view of learning in a taxonomic framework, we consider two types of trees. First, a FAME tree is constructed with a supervised divisive clustering algorithm. Subsequently, based on 16S rRNA gene sequence analysis, phylogenetic trees are inferred by the NJ and UPGMA methods. In this second approach, the species classification problem is based on the combination of two different types of data. Herein, 16S rRNA gene sequence data is used for phylogenetic tree inference and the corresponding binary tree splits are learned based on FAME data. We call this learning approach 'phylogenetic learning'. Supervised Random Forest models are developed to train the classification tasks in a stratified cross-validation setting. In this way, better classification results are obtained for species that are typically hard to distinguish by a single or flat multi-class classification model. Conclusions FAME-based bacterial species classification is successfully evaluated in a taxonomic framework. Although the proposed approach does not improve the overall accuracy compared to flat multi-class classification, it has some distinct advantages. First, it has better capabilities for distinguishing species on which flat multi-class classification fails. Secondly, the hierarchical classification structure allows to easily evaluate and visualize the resolution of FAME data for

From learning taxonomies to phylogenetic learning: Integration of 16S rRNA gene data into FAME-based bacterial classification

Directory of Open Access Journals (Sweden)

Dawyndt Peter

2010-01-01

Full Text Available Abstract Background Machine learning techniques have shown to improve bacterial species classification based on fatty acid methyl ester (FAME data. Nonetheless, FAME analysis has a limited resolution for discrimination of bacteria at the species level. In this paper, we approach the species classification problem from a taxonomic point of view. Such a taxonomy or tree is typically obtained by applying clustering algorithms on FAME data or on 16S rRNA gene data. The knowledge gained from the tree can then be used to evaluate FAME-based classifiers, resulting in a novel framework for bacterial species classification. Results In view of learning in a taxonomic framework, we consider two types of trees. First, a FAME tree is constructed with a supervised divisive clustering algorithm. Subsequently, based on 16S rRNA gene sequence analysis, phylogenetic trees are inferred by the NJ and UPGMA methods. In this second approach, the species classification problem is based on the combination of two different types of data. Herein, 16S rRNA gene sequence data is used for phylogenetic tree inference and the corresponding binary tree splits are learned based on FAME data. We call this learning approach 'phylogenetic learning'. Supervised Random Forest models are developed to train the classification tasks in a stratified cross-validation setting. In this way, better classification results are obtained for species that are typically hard to distinguish by a single or flat multi-class classification model. Conclusions FAME-based bacterial species classification is successfully evaluated in a taxonomic framework. Although the proposed approach does not improve the overall accuracy compared to flat multi-class classification, it has some distinct advantages. First, it has better capabilities for distinguishing species on which flat multi-class classification fails. Secondly, the hierarchical classification structure allows to easily evaluate and visualize the

From learning taxonomies to phylogenetic learning: integration of 16S rRNA gene data into FAME-based bacterial classification.

Science.gov (United States)

Slabbinck, Bram; Waegeman, Willem; Dawyndt, Peter; De Vos, Paul; De Baets, Bernard

2010-01-30

Machine learning techniques have shown to improve bacterial species classification based on fatty acid methyl ester (FAME) data. Nonetheless, FAME analysis has a limited resolution for discrimination of bacteria at the species level. In this paper, we approach the species classification problem from a taxonomic point of view. Such a taxonomy or tree is typically obtained by applying clustering algorithms on FAME data or on 16S rRNA gene data. The knowledge gained from the tree can then be used to evaluate FAME-based classifiers, resulting in a novel framework for bacterial species classification. In view of learning in a taxonomic framework, we consider two types of trees. First, a FAME tree is constructed with a supervised divisive clustering algorithm. Subsequently, based on 16S rRNA gene sequence analysis, phylogenetic trees are inferred by the NJ and UPGMA methods. In this second approach, the species classification problem is based on the combination of two different types of data. Herein, 16S rRNA gene sequence data is used for phylogenetic tree inference and the corresponding binary tree splits are learned based on FAME data. We call this learning approach 'phylogenetic learning'. Supervised Random Forest models are developed to train the classification tasks in a stratified cross-validation setting. In this way, better classification results are obtained for species that are typically hard to distinguish by a single or flat multi-class classification model. FAME-based bacterial species classification is successfully evaluated in a taxonomic framework. Although the proposed approach does not improve the overall accuracy compared to flat multi-class classification, it has some distinct advantages. First, it has better capabilities for distinguishing species on which flat multi-class classification fails. Secondly, the hierarchical classification structure allows to easily evaluate and visualize the resolution of FAME data for the discrimination of bacterial

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.

Inferring microbial interaction networks from metagenomic data using SgLV-EKF algorithm.

Science.gov (United States)

Alshawaqfeh, Mustafa; Serpedin, Erchin; Younes, Ahmad Bani

2017-03-27

Inferring the microbial interaction networks (MINs) and modeling their dynamics are critical in understanding the mechanisms of the bacterial ecosystem and designing antibiotic and/or probiotic therapies. Recently, several approaches were proposed to infer MINs using the generalized Lotka-Volterra (gLV) model. Main drawbacks of these models include the fact that these models only consider the measurement noise without taking into consideration the uncertainties in the underlying dynamics. Furthermore, inferring the MIN is characterized by the limited number of observations and nonlinearity in the regulatory mechanisms. Therefore, novel estimation techniques are needed to address these challenges. This work proposes SgLV-EKF: a stochastic gLV model that adopts the extended Kalman filter (EKF) algorithm to model the MIN dynamics. In particular, SgLV-EKF employs a stochastic modeling of the MIN by adding a noise term to the dynamical model to compensate for modeling uncertainties. This stochastic modeling is more realistic than the conventional gLV model which assumes that the MIN dynamics are perfectly governed by the gLV equations. After specifying the stochastic model structure, we propose the EKF to estimate the MIN. SgLV-EKF was compared with two similarity-based algorithms, one algorithm from the integral-based family and two regression-based algorithms, in terms of the achieved performance on two synthetic data-sets and two real data-sets. The first data-set models the randomness in measurement data, whereas, the second data-set incorporates uncertainties in the underlying dynamics. The real data-sets are provided by a recent study pertaining to an antibiotic-mediated Clostridium difficile infection. The experimental results demonstrate that SgLV-EKF outperforms the alternative methods in terms of robustness to measurement noise, modeling errors, and tracking the dynamics of the MIN. Performance analysis demonstrates that the proposed SgLV-EKF algorithm

A New Pansharpening Method Based on Spatial and Spectral Sparsity Priors.

Science.gov (United States)

He, Xiyan; Condat, Laurent; Bioucas-Diaz, Jose; Chanussot, Jocelyn; Xia, Junshi

2014-06-27

The development of multisensor systems in recent years has led to great increase in the amount of available remote sensing data. Image fusion techniques aim at inferring high quality images of a given area from degraded versions of the same area obtained by multiple sensors. This paper focuses on pansharpening, which is the inference of a high spatial resolution multispectral image from two degraded versions with complementary spectral and spatial resolution characteristics: a) a low spatial resolution multispectral image; and b) a high spatial resolution panchromatic image. We introduce a new variational model based on spatial and spectral sparsity priors for the fusion. In the spectral domain we encourage low-rank structure, whereas in the spatial domain we promote sparsity on the local differences. Given the fact that both panchromatic and multispectral images are integrations of the underlying continuous spectra using different channel responses, we propose to exploit appropriate regularizations based on both spatial and spectral links between panchromatic and the fused multispectral images. A weighted version of the vector Total Variation (TV) norm of the data matrix is employed to align the spatial information of the fused image with that of the panchromatic image. With regard to spectral information, two different types of regularization are proposed to promote a soft constraint on the linear dependence between the panchromatic and the fused multispectral images. The first one estimates directly the linear coefficients from the observed panchromatic and low resolution multispectral images by Linear Regression (LR) while the second one employs the Principal Component Pursuit (PCP) to obtain a robust recovery of the underlying low-rank structure. We also show that the two regularizers are strongly related. The basic idea of both regularizers is that the fused image should have low-rank and preserve edge locations. We use a variation of the recently proposed

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.

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

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.

The stock-flow model of spatial data infrastructure development refined by fuzzy logic.

Science.gov (United States)

Abdolmajidi, Ehsan; Harrie, Lars; Mansourian, Ali

2016-01-01

The system dynamics technique has been demonstrated to be a proper method by which to model and simulate the development of spatial data infrastructures (SDI). An SDI is a collaborative effort to manage and share spatial data at different political and administrative levels. It is comprised of various dynamically interacting quantitative and qualitative (linguistic) variables. To incorporate linguistic variables and their joint effects in an SDI-development model more effectively, we suggest employing fuzzy logic. Not all fuzzy models are able to model the dynamic behavior of SDIs properly. Therefore, this paper aims to investigate different fuzzy models and their suitability for modeling SDIs. To that end, two inference and two defuzzification methods were used for the fuzzification of the joint effect of two variables in an existing SDI model. The results show that the Average-Average inference and Center of Area defuzzification can better model the dynamics of SDI development.

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.

Spatial transcriptomes within the Pseudomonas aeruginosa biofilm architecture.

Science.gov (United States)

Heacock-Kang, Yun; Sun, Zhenxin; Zarzycki-Siek, Jan; McMillan, Ian A; Norris, Michael H; Bluhm, Andrew P; Cabanas, Darlene; Fogen, Dawson; Vo, Hung; Donachie, Stuart P; Borlee, Bradley R; Sibley, Christopher D; Lewenza, Shawn; Schurr, Michael J; Schweizer, Herbert P; Hoang, Tung T

2017-12-01

Bacterial cooperative associations and dynamics in biofilm microenvironments are of special interest in recent years. Knowledge of localized gene-expression and corresponding bacterial behaviors within the biofilm architecture at a global scale has been limited, due to a lack of robust technology to study limited number of cells in stratified layers of biofilms. With our recent pioneering developments in single bacterial cell transcriptomic analysis technology, we generated herein an unprecedented spatial transcriptome map of the mature in vitro Pseudomonas aeruginosa biofilm model, revealing contemporaneous yet altered bacterial behaviors at different layers within the biofilm architecture (i.e., surface, middle and interior of the biofilm). Many genes encoding unknown functions were highly expressed at the biofilm-solid interphase, exposing a critical gap in the knowledge of their activities that may be unique to this interior niche. Several genes of unknown functions are critical for biofilm formation. The in vivo importance of these unknown proteins was validated in invertebrate (fruit fly) and vertebrate (mouse) models. We envisage the future value of this report to the community, in aiding the further pathophysiological understanding of P. aeruginosa biofilms. Our approach will open doors to the study of bacterial functional genomics of different species in numerous settings. © 2017 The Authors. Molecular Microbiology Published by John Wiley & Sons Ltd.

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.

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.

Archaeal and Bacterial Communities Associated with the Surface Mucus of Caribbean Corals Differ in Their Degree of Host Specificity and Community Turnover Over Reefs.

Science.gov (United States)

Frade, Pedro R; Roll, Katharina; Bergauer, Kristin; Herndl, Gerhard J

2016-01-01

Comparative studies on the distribution of archaeal versus bacterial communities associated with the surface mucus layer of corals have rarely taken place. It has therefore remained enigmatic whether mucus-associated archaeal and bacterial communities exhibit a similar specificity towards coral hosts and whether they vary in the same fashion over spatial gradients and between reef locations. We used microbial community profiling (terminal-restriction fragment length polymorphism, T-RFLP) and clone library sequencing of the 16S rRNA gene to compare the diversity and community structure of dominant archaeal and bacterial communities associating with the mucus of three common reef-building coral species (Porites astreoides, Siderastrea siderea and Orbicella annularis) over different spatial scales on a Caribbean fringing reef. Sampling locations included three reef sites, three reef patches within each site and two depths. Reference sediment samples and ambient water were also taken for each of the 18 sampling locations resulting in a total of 239 samples. While only 41% of the bacterial operational taxonomic units (OTUs) characterized by T-RFLP were shared between mucus and the ambient water or sediment, for archaeal OTUs this percentage was 2-fold higher (78%). About half of the mucus-associated OTUs (44% and 58% of bacterial and archaeal OTUs, respectively) were shared between the three coral species. Our multivariate statistical analysis (ANOSIM, PERMANOVA and CCA) showed that while the bacterial community composition was determined by habitat (mucus, sediment or seawater), host coral species, location and spatial distance, the archaeal community composition was solely determined by the habitat. This study highlights that mucus-associated archaeal and bacterial communities differ in their degree of community turnover over reefs and in their host-specificity.

Pervasive Selection for Cooperative Cross-Feeding in Bacterial Communities.

Directory of Open Access Journals (Sweden)

Sebastian Germerodt

2016-06-01

Full Text Available Bacterial communities are taxonomically highly diverse, yet the mechanisms that maintain this diversity remain poorly understood. We hypothesized that an obligate and mutual exchange of metabolites, as is very common among bacterial cells, could stabilize different genotypes within microbial communities. To test this, we developed a cellular automaton to model interactions among six empirically characterized genotypes that differ in their ability and propensity to produce amino acids. By systematically varying intrinsic (i.e. benefit-to-cost ratio and extrinsic parameters (i.e. metabolite diffusion level, environmental amino acid availability, we show that obligate cross-feeding of essential metabolites is selected for under a broad range of conditions. In spatially structured environments, positive assortment among cross-feeders resulted in the formation of cooperative clusters, which limited exploitation by non-producing auxotrophs, yet allowed them to persist at the clusters' periphery. Strikingly, cross-feeding helped to maintain genotypic diversity within populations, while amino acid supplementation to the environment decoupled obligate interactions and favored auxotrophic cells that saved amino acid production costs over metabolically autonomous prototrophs. Together, our results suggest that spatially structured environments and limited nutrient availabilities should facilitate the evolution of metabolic interactions, which can help to maintain genotypic diversity within natural microbial populations.

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.

Comparison of Channel Catfish and Blue Catfish Gut Microbiota Assemblages Shows Minimal Effects of Host Genetics on Microbial Structure and Inferred Function

Directory of Open Access Journals (Sweden)

Jacob W. Bledsoe

2018-05-01

Full Text Available The microbiota of teleost fish has gained a great deal of research attention within the past decade, with experiments suggesting that both host-genetics and environment are strong ecological forces shaping the bacterial assemblages of fish microbiomes. Despite representing great commercial and scientific importance, the catfish within the family Ictaluridae, specifically the blue and channel catfish, have received very little research attention directed toward their gut-associated microbiota using 16S rRNA gene sequencing. Within this study we utilize multiple genetically distinct strains of blue and channel catfish, verified via microsatellite genotyping, to further quantify the role of host-genetics in shaping the bacterial communities in the fish gut, while maintaining environmental and husbandry parameters constant. Comparisons of the gut microbiota among the two catfish species showed no differences in bacterial species richness (observed and Chao1 or overall composition (weighted and unweighted UniFrac and UniFrac distances showed no correlation with host genetic distances (Rst according to Mantel tests. The microbiota of environmental samples (diet and water were found to be significantly more diverse than that of the catfish gut associated samples, suggesting that factors within the host were further regulating the bacterial communities, despite the lack of a clear connection between microbiota composition and host genotype. The catfish gut communities were dominated by the phyla Fusobacteria, Proteobacteria, and Firmicutes; however, differential abundance analysis between the two catfish species using analysis of composition of microbiomes detected two differential genera, Cetobacterium and Clostridium XI. The metagenomic pathway features inferred from our dataset suggests the catfish gut bacterial communities possess pathways beneficial to their host such as those involved in nutrient metabolism and antimicrobial biosynthesis, while

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

Effects of remediation on the bacterial community of an acid mine drainage impacted stream.

Science.gov (United States)

Ghosh, Suchismita; Moitra, Moumita; Woolverton, Christopher J; Leff, Laura G

2012-11-01

Acid mine drainage (AMD) represents a global threat to water resources, and as such, remediation of AMD-impacted streams is a common practice. During this study, we examined bacterial community structure and environmental conditions in a low-order AMD-impacted stream before, during, and after remediation. Bacterial community structure was examined via polymerase chain reaction amplification of 16S rRNA genes followed by denaturing gradient gel electrophoresis. Also, bacterial abundance and physicochemical data (including metal concentrations) were collected and relationships to bacterial community structure were determined using BIO-ENV analysis. Remediation of the study stream altered environmental conditions, including pH and concentrations of some metals, and consequently, the bacterial community changed. However, remediation did not necessarily restore the stream to conditions found in the unimpacted reference stream; for example, bacterial abundances and concentrations of some elements, such as sulfur, magnesium, and manganese, were different in the remediated stream than in the reference stream. BIO-ENV analysis revealed that changes in pH and iron concentration, associated with remediation, primarily explained temporal alterations in bacterial community structure. Although the sites sampled in the remediated stream were in relatively close proximity to each other, spatial variation in community composition suggests that differences in local environmental conditions may have large impacts on the microbial assemblage.

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.

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

Bacterial community composition and potential driving factors in different reef habitats of the Spermonde Archipelago, Indonesia

DEFF Research Database (Denmark)

Kegler, Hauke F.; Lukman, Muhammad; Teichberg, Mirta

2017-01-01

Coastal eutrophication is a key driver of shifts in bacterial communities on coral reefs. With fringing and patch reefs at varying distances from the coast the Spermonde Archipelago in southern Sulawesi, Indonesia offers ideal conditions to study the effects of coastal eutrophication along...... a spatially defined gradient. The present study investigated bacterial community composition of three coral reef habitats: the water column, sediments, and mucus of the hard coral genus Fungia, along that cross shelf environmental and water quality gradient. The main research questions were: (1) How do water....../Shigella (Gammaproteobacteria) and Raistonia (Betaproteobacteria), respectively, both dominated the bacterial community composition of the both size fractions of the water column and coral mucus. The sampled reef sediments were more diverse, and no single OTUs was dominant. There was no gradual shift in bacterial classes...

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.

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.

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.

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

Balanced Fertilization Decreases Environmental Filtering on Soil Bacterial Community Assemblage in North China

Directory of Open Access Journals (Sweden)

Youzhi Feng

2017-12-01

Full Text Available Although increasing evidences have emerged for responses of soil microorganisms to fertilizations, the knowledge regarding community assemblages that cause variations in composition is still lacking, as well as the possible feedback to soil fertility. Phylogenetic conservatism of species indicates their similar environmental preferences and/or function traits and phylogenetic signals further can infer community assemblages and influenced ecological processes. Here, we calculated the mean pairwise phylogenetic distance and nearest relative index, characterizing phylogenetic signal and the undergone ecological process to evaluate the community assembly of soil bacterial phylotypes in 20-year fertilized soils. The bacterial community assembly is structured by environmental filtering, regardless of fertilization regime. Soil phosphorous (P availability imposes selection on community assemblage and influences their community turnover among fertilizations. When P nutrient lacks, the effect of environmental filtering becomes stronger, hence bacterial functional traits become more coherent; this process results into increased intraspecific interactions characterized by co-occurrence network analysis. In contrast, when P nutrient becomes abundant, the environmental selection is mitigated; function traits are evened. This process reduces intraspecific interactions and increases carbon sequestration efficiency, which is finally of great favor to the increases in soil fertility. This study has made the first attempt, at the bacterial level, to understand how fertilization affects agroecosystems. When more phylogenetic information on how nutrient cycling-related microbes respond to fertilization becomes available, the systematic knowledge will eventually provide guidance to optimal fertilization strategies.

Evaluating the effects of variable water chemistry on bacterial transport during infiltration.

Science.gov (United States)

Zhang, Haibo; Nordin, Nahjan Amer; Olson, Mira S

2013-07-01

Bacterial infiltration through the subsurface has been studied experimentally under different conditions of interest and is dependent on a variety of physical, chemical and biological factors. However, most bacterial transport studies fail to adequately represent the complex processes occurring in natural systems. Bacteria are frequently detected in stormwater runoff, and may present risk of microbial contamination during stormwater recharge into groundwater. Mixing of stormwater runoff with groundwater during infiltration results in changes in local solution chemistry, which may lead to changes in both bacterial and collector surface properties and subsequent bacterial attachment rates. This study focuses on quantifying changes in bacterial transport behavior under variable solution chemistry, and on comparing the influences of chemical variability and physical variability on bacterial attachment rates. Bacterial attachment rate at the soil-water interface was predicted analytically using a combined rate equation, which varies temporally and spatially with respect to changes in solution chemistry. Two-phase Monte Carlo analysis was conducted and an overall input-output correlation coefficient was calculated to quantitatively describe the importance of physiochemical variation on the estimates of attachment rate. Among physical variables, soil particle size has the highest correlation coefficient, followed by porosity of the soil media, bacterial size and flow velocity. Among chemical variables, ionic strength has the highest correlation coefficient. A semi-reactive microbial transport model was developed within HP1 (HYDRUS1D-PHREEQC) and applied to column transport experiments with constant and variable solution chemistries. Bacterial attachment rates varied from 9.10×10(-3)min(-1) to 3.71×10(-3)min(-1) due to mixing of synthetic stormwater (SSW) with artificial groundwater (AGW), while bacterial attachment remained constant at 9.10×10(-3)min(-1) in a constant

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...

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.

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...

Archaeal and Bacterial Communities Associated with the Surface Mucus of Caribbean Corals Differ in Their Degree of Host Specificity and Community Turnover Over Reefs.

Directory of Open Access Journals (Sweden)

Pedro R Frade

Full Text Available Comparative studies on the distribution of archaeal versus bacterial communities associated with the surface mucus layer of corals have rarely taken place. It has therefore remained enigmatic whether mucus-associated archaeal and bacterial communities exhibit a similar specificity towards coral hosts and whether they vary in the same fashion over spatial gradients and between reef locations. We used microbial community profiling (terminal-restriction fragment length polymorphism, T-RFLP and clone library sequencing of the 16S rRNA gene to compare the diversity and community structure of dominant archaeal and bacterial communities associating with the mucus of three common reef-building coral species (Porites astreoides, Siderastrea siderea and Orbicella annularis over different spatial scales on a Caribbean fringing reef. Sampling locations included three reef sites, three reef patches within each site and two depths. Reference sediment samples and ambient water were also taken for each of the 18 sampling locations resulting in a total of 239 samples. While only 41% of the bacterial operational taxonomic units (OTUs characterized by T-RFLP were shared between mucus and the ambient water or sediment, for archaeal OTUs this percentage was 2-fold higher (78%. About half of the mucus-associated OTUs (44% and 58% of bacterial and archaeal OTUs, respectively were shared between the three coral species. Our multivariate statistical analysis (ANOSIM, PERMANOVA and CCA showed that while the bacterial community composition was determined by habitat (mucus, sediment or seawater, host coral species, location and spatial distance, the archaeal community composition was solely determined by the habitat. This study highlights that mucus-associated archaeal and bacterial communities differ in their degree of community turnover over reefs and in their host-specificity.

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…

Identifying elementary iterated systems through algorithmic inference: The Cantor set example

Energy Technology Data Exchange (ETDEWEB)

Apolloni, Bruno [Dipartimento di Scienze dell' Informazione, Universita degli Studi di Milano, Via Comelico 39/41, 20135 Milan (Italy)]. E-mail: apolloni@dsi.unimi.it; Bassis, Simone [Dipartimento di Scienze dell' Informazione, Universita degli Studi di Milano, Via Comelico 39/41, 20135 Milan (Italy)]. E-mail: bassis@dsi.unimi.it

2006-10-15

We come back to the old problem of fractal identification within the new framework of algorithmic Inference. The key points are: (i) to identify sufficient statistics to be put in connection with the unknown values of the fractal parameters, and (ii) to manage the timing of the iterated process through spatial statistics. We fill these tasks successfully with the Cantor sets. We are able to compute confidence intervals for both the scaling parameter {theta} and the iteration number n at which we are observing a set. We both check numerically the coverage of these intervals and delineate a general strategy for affording more complex iterated systems.

Patterns of coexistence of two species of freshwater turtles are affected by spatial scale

DEFF Research Database (Denmark)

Segurado, P.; Kunin, W.E.; Filipe, A.F.

2012-01-01

Inferring biotic interactions from the examination of patterns of species occurrences has been a central tenet in community ecology, and it has recently gained interest in the context of single-species distribution modelling. However, understanding of how spatial extent and grain size affect such...

Temporal and spatial changes of microbial community in an industrial effluent receiving area in Hangzhou Bay.

Science.gov (United States)

Zhang, Yan; Chen, Lujun; Sun, Renhua; Dai, Tianjiao; Tian, Jinping; Zheng, Wei; Wen, Donghui

2016-06-01

Anthropogenic activities usually contaminate water environments, and have led to the eutrophication of many estuaries and shifts in microbial communities. In this study, the temporal and spatial changes of the microbial community in an industrial effluent receiving area in Hangzhou Bay were investigated by 454 pyrosequencing. The bacterial community showed higher richness and biodiversity than the archaeal community in all sediments. Proteobacteria dominated in the bacterial communities of all the samples; Marine_Group_I and Methanomicrobia were the two dominant archaeal classes in the effluent receiving area. PCoA and AMOVA revealed strong seasonal but minor spatial changes in both bacterial and archaeal communities in the sediments. The seasonal changes of the bacterial community were less significant than those of the archaeal community, which mainly consisted of fluctuations in abundance of a large proportion of longstanding species rather than the appearance and disappearance of major archaeal species. Temperature was found to positively correlate with the dominant bacteria, Betaproteobacteria, and negatively correlate with the dominant archaea, Marine_Group_I; and might be the primary driving force for the seasonal variation of the microbial community. Copyright © 2016. Published by Elsevier B.V.

Exploring the effects of photon correlations from thermal sources on bacterial photosynthesis

Directory of Open Access Journals (Sweden)

Pedro D. Manrique

Full Text Available Thermal light sources can produce photons with strong spatial correlations. We study the role that these correlations might potentially play in bacterial photosynthesis. Our findings show a relationship between the transversal distance between consecutive absorptions and the efficiency of the photosynthetic process. Furthermore, membranes where the clustering of core complexes (so-called RC-LH1 is high, display a range where the organism profits maximally from the spatial correlation of the incoming light. By contrast, no maximum is found for membranes with low core-core clustering. We employ a detailed membrane model with state-of-the-art empirical inputs. Our results suggest that the organization of the membrane’s antenna complexes may be well-suited to the spatial correlations present in an natural light source. Future experiments will be needed to test this prediction. Keywords: Photo-bunching, Spatial correlation, Photosynthesis, Purple bacteria

Indexing the Environmental Quality Performance Based on A Fuzzy Inference Approach

Science.gov (United States)

Iswari, Lizda

2018-03-01

Environmental performance strongly deals with the quality of human life. In Indonesia, this performance is quantified through Environmental Quality Index (EQI) which consists of three indicators, i.e. river quality index, air quality index, and coverage of land cover. The current of this instrument data processing was done by averaging and weighting each index to represent the EQI at the provincial level. However, we found EQI interpretations that may contain some uncertainties and have a range of circumstances possibly less appropriate if processed under a common statistical approach. In this research, we aim to manage the indicators of EQI with a more intuitive computation technique and make some inferences related to the environmental performance in 33 provinces in Indonesia. Research was conducted in three stages of Mamdani Fuzzy Inference System (MAFIS), i.e. fuzzification, data inference, and defuzzification. Data input consists of 10 environmental parameters and the output is an index of Environmental Quality Performance (EQP). Research was applied to the environmental condition data set in 2015 and quantified the results into the scale of 0 to 100, i.e. 10 provinces at good performance with the EQP above 80 dominated by provinces in eastern part of Indonesia, 22 provinces with the EQP between 80 to 50, and one province in Java Island with the EQP below 20. This research shows that environmental quality performance can be quantified without eliminating the natures of the data set and simultaneously is able to show the environment behavior along with its spatial pattern distribution.

Using Spatial Clustering in Forecasting Groundwater Quality Parameters by ANFIS

Directory of Open Access Journals (Sweden)

MohammadTaghi Alami

2016-07-01

Full Text Available Groundwater is a major source of water supply for domestic, agricultural, and industrial uses; hence, its quality modeling is an important task in hydro-environmental studies. While many data-based models have been developed for this purpose, the performance of such data-based models can be drastically enhanced if they are based on temporal and spatial pre-processing. In this study, geostatistics tools (e.g., Co-Kriging, as spatial estimators, and self-organizing map (SOM, as a clustering technique, were employed in conjunction with Adaptive Neuro-Fuzzy Inference System (ANFIS for the temporal forecasting of such quality parameters as electrical conductivity (EC and total dissolved solids (TDS of the groundwater in Ardabil Plain. Using the results thus obtained, the impact of spatial data clustering was also investigated on the same parameters. The results showed that, if propoer input data are selected, the proposed spatial clustering technique is capable of imporving groundwater quality forecasts made by ANFIS.

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...

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.

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.

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

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...

Accounting for Non-Gaussian Sources of Spatial Correlation in Parametric Functional Magnetic Resonance Imaging Paradigms II: A Method to Obtain First-Level Analysis Residuals with Uniform and Gaussian Spatial Autocorrelation Function and Independent and Identically Distributed Time-Series.

Science.gov (United States)

Gopinath, Kaundinya; Krishnamurthy, Venkatagiri; Lacey, Simon; Sathian, K

2018-02-01

In a recent study Eklund et al. have shown that cluster-wise family-wise error (FWE) rate-corrected inferences made in parametric statistical method-based functional magnetic resonance imaging (fMRI) studies over the past couple of decades may have been invalid, particularly for cluster defining thresholds less stringent than p functions (sACFs) of fMRI data had been modeled incorrectly to follow a Gaussian form, whereas empirical data suggest otherwise. Hence, the residuals from general linear model (GLM)-based fMRI activation estimates in these studies may not have possessed a homogenously Gaussian sACF. Here we propose a method based on the assumption that heterogeneity and non-Gaussianity of the sACF of the first-level GLM analysis residuals, as well as temporal autocorrelations in the first-level voxel residual time-series, are caused by unmodeled MRI signal from neuronal and physiological processes as well as motion and other artifacts, which can be approximated by appropriate decompositions of the first-level residuals with principal component analysis (PCA), and removed. We show that application of this method yields GLM residuals with significantly reduced spatial correlation, nearly Gaussian sACF and uniform spatial smoothness across the brain, thereby allowing valid cluster-based FWE-corrected inferences based on assumption of Gaussian spatial noise. We further show that application of this method renders the voxel time-series of first-level GLM residuals independent, and identically distributed across time (which is a necessary condition for appropriate voxel-level GLM inference), without having to fit ad hoc stochastic colored noise models. Furthermore, the detection power of individual subject brain activation analysis is enhanced. This method will be especially useful for case studies, which rely on first-level GLM analysis inferences.

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

Aspect has a greater impact on alpine soil bacterial community structure than elevation.

Science.gov (United States)

Wu, Jieyun; Anderson, Barbara J; Buckley, Hannah L; Lewis, Gillian; Lear, Gavin

2017-03-01

Gradients in environmental conditions, including climate factors and resource availability, occur along mountain inclines, providing a 'natural laboratory' to explore their combined impacts on microbial distributions. Conflicting spatial patterns observed across elevation gradients in soil bacterial community structure suggest that they are driven by various interacting factors at different spatial scales. Here, we investigated the relative impacts of non-resource (e.g. soil temperature, pH) and resource conditions (e.g. soil carbon and nitrogen) on the biogeography of soil bacterial communities across broad (i.e. along a 1500 m mountain elevation gradient) and fine sampling scales (i.e. along sunny and shady aspects of a mountain ridge). Our analysis of 16S rRNA gene data confirmed that when sampling across distances of soil pH. These findings highlight the need to incorporate knowledge of multiple factors, including site aspect and soil pH for the appropriate use of elevation gradients as a proxy to explore the impacts of climate change on microbial community composition. © FEMS 2016. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Exploring the effects of photon correlations from thermal sources on bacterial photosynthesis

Science.gov (United States)

Manrique, Pedro D.; Caycedo-Soler, Felipe; De Mendoza, Adriana; Rodríguez, Ferney; Quiroga, Luis; Johnson, Neil F.

Thermal light sources can produce photons with strong spatial correlations. We study the role that these correlations might potentially play in bacterial photosynthesis. Our findings show a relationship between the transversal distance between consecutive absorptions and the efficiency of the photosynthetic process. Furthermore, membranes where the clustering of core complexes (so-called RC-LH1) is high, display a range where the organism profits maximally from the spatial correlation of the incoming light. By contrast, no maximum is found for membranes with low core-core clustering. We employ a detailed membrane model with state-of-the-art empirical inputs. Our results suggest that the organization of the membrane's antenna complexes may be well-suited to the spatial correlations present in an natural light source. Future experiments will be needed to test this prediction.

Features of the bronchial bacterial microbiome associated with atopy, asthma, and responsiveness to inhaled corticosteroid treatment.

Science.gov (United States)

Durack, Juliana; Lynch, Susan V; Nariya, Snehal; Bhakta, Nirav R; Beigelman, Avraham; Castro, Mario; Dyer, Anne-Marie; Israel, Elliot; Kraft, Monica; Martin, Richard J; Mauger, David T; Rosenberg, Sharon R; Sharp-King, Tonya; White, Steven R; Woodruff, Prescott G; Avila, Pedro C; Denlinger, Loren C; Holguin, Fernando; Lazarus, Stephen C; Lugogo, Njira; Moore, Wendy C; Peters, Stephen P; Que, Loretta; Smith, Lewis J; Sorkness, Christine A; Wechsler, Michael E; Wenzel, Sally E; Boushey, Homer A; Huang, Yvonne J

2017-07-01

Compositional differences in the bronchial bacterial microbiota have been associated with asthma, but it remains unclear whether the findings are attributable to asthma, to aeroallergen sensitization, or to inhaled corticosteroid treatment. We sought to compare the bronchial bacterial microbiota in adults with steroid-naive atopic asthma, subjects with atopy but no asthma, and nonatopic healthy control subjects and to determine relationships of the bronchial microbiota to phenotypic features of asthma. Bacterial communities in protected bronchial brushings from 42 atopic asthmatic subjects, 21 subjects with atopy but no asthma, and 21 healthy control subjects were profiled by using 16S rRNA gene sequencing. Bacterial composition and community-level functions inferred from sequence profiles were analyzed for between-group differences. Associations with clinical and inflammatory variables were examined, including markers of type 2-related inflammation and change in airway hyperresponsiveness after 6 weeks of fluticasone treatment. The bronchial microbiome differed significantly among the 3 groups. Asthmatic subjects were uniquely enriched in members of the Haemophilus, Neisseria, Fusobacterium, and Porphyromonas species and the Sphingomonodaceae family and depleted in members of the Mogibacteriaceae family and Lactobacillales order. Asthma-associated differences in predicted bacterial functions included involvement of amino acid and short-chain fatty acid metabolism pathways. Subjects with type 2-high asthma harbored significantly lower bronchial bacterial burden. Distinct changes in specific microbiota members were seen after fluticasone treatment. Steroid responsiveness was linked to differences in baseline compositional and functional features of the bacterial microbiome. Even in subjects with mild steroid-naive asthma, differences in the bronchial microbiome are associated with immunologic and clinical features of the disease. The specific differences identified

Effect of flow and peristaltic mixing on bacterial growth in a gut-like channel

Science.gov (United States)

Cremer, Jonas; Segota, Igor; Yang, Chih-yu; Arnoldini, Markus; Sauls, John T.; Zhang, Zhongge; Gutierrez, Edgar; Groisman, Alex; Hwa, Terence

2016-01-01

The ecology of microbes in the gut has been shown to play important roles in the health of the host. To better understand microbial growth and population dynamics in the proximal colon, the primary region of bacterial growth in the gut, we built and applied a fluidic channel that we call the “minigut.” This is a channel with an array of membrane valves along its length, which allows mimicking active contractions of the colonic wall. Repeated contraction is shown to be crucial in maintaining a steady-state bacterial population in the device despite strong flow along the channel that would otherwise cause bacterial washout. Depending on the flow rate and the frequency of contractions, the bacterial density profile exhibits varying spatial dependencies. For a synthetic cross-feeding community, the species abundance ratio is also strongly affected by mixing and flow along the length of the device. Complex mixing dynamics due to contractions is described well by an effective diffusion term. Bacterial dynamics is captured by a simple reaction–diffusion model without adjustable parameters. Our results suggest that flow and mixing play a major role in shaping the microbiota of the colon. PMID:27681630

Non-Stationary Dependence Structures for Spatial Extremes

KAUST Repository

Huser, Raphaël

2016-03-03

Max-stable processes are natural models for spatial extremes because they provide suitable asymptotic approximations to the distribution of maxima of random fields. In the recent past, several parametric families of stationary max-stable models have been developed, and fitted to various types of data. However, a recurrent problem is the modeling of non-stationarity. In this paper, we develop non-stationary max-stable dependence structures in which covariates can be easily incorporated. Inference is performed using pairwise likelihoods, and its performance is assessed by an extensive simulation study based on a non-stationary locally isotropic extremal t model. Evidence that unknown parameters are well estimated is provided, and estimation of spatial return level curves is discussed. The methodology is demonstrated with temperature maxima recorded over a complex topography. Models are shown to satisfactorily capture extremal dependence.

Exploiting rRNA operon copy number to investigate bacterial reproductive strategies.

Science.gov (United States)

Roller, Benjamin R K; Stoddard, Steven F; Schmidt, Thomas M

2016-09-12

The potential for rapid reproduction is a hallmark of microbial life, but microbes in nature must also survive and compete when growth is constrained by resource availability. Successful reproduction requires different strategies when resources are scarce and when they are abundant 1,2 , but a systematic framework for predicting these reproductive strategies in bacteria has not been available. Here, we show that the number of ribosomal RNA operons (rrn) in bacterial genomes predicts two important components of reproduction-growth rate and growth efficiency-which are favoured under contrasting regimes of resource availability 3,4 . We find that the maximum reproductive rate of bacteria doubles with a doubling of rrn copy number, and the efficiency of carbon use is inversely related to maximal growth rate and rrn copy number. We also identify a feasible explanation for these patterns: the rate and yield of protein synthesis mirror the overall pattern in maximum growth rate and growth efficiency. Furthermore, comparative analysis of genomes from 1,167 bacterial species reveals that rrn copy number predicts traits associated with resource availability, including chemotaxis and genome streamlining. Genome-wide patterns of orthologous gene content covary with rrn copy number, suggesting convergent evolution in response to resource availability. Our findings imply that basic cellular processes adapt in contrasting ways to long-term differences in resource availability. They also establish a basis for predicting changes in bacterial community composition in response to resource perturbations using rrn copy number measurements 5 or inferences 6,7 .

Quantum-Like Representation of Non-Bayesian Inference

Science.gov (United States)

Asano, M.; Basieva, I.; Khrennikov, A.; Ohya, M.; Tanaka, Y.

2013-01-01

This research is related to the problem of "irrational decision making or inference" that have been discussed in cognitive psychology. There are some experimental studies, and these statistical data cannot be described by classical probability theory. The process of decision making generating these data cannot be reduced to the classical Bayesian inference. For this problem, a number of quantum-like coginitive models of decision making was proposed. Our previous work represented in a natural way the classical Bayesian inference in the frame work of quantum mechanics. By using this representation, in this paper, we try to discuss the non-Bayesian (irrational) inference that is biased by effects like the quantum interference. Further, we describe "psychological factor" disturbing "rationality" as an "environment" correlating with the "main system" of usual Bayesian inference.

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...

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

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

A Bayesian Framework for Analysis of Pseudo-Spatial Models of Comparable Engineered Systems with Application to Spacecraft Anomaly Prediction Based on Precedent Data

Science.gov (United States)

Ndu, Obibobi Kamtochukwu

To ensure that estimates of risk and reliability inform design and resource allocation decisions in the development of complex engineering systems, early engagement in the design life cycle is necessary. An unfortunate constraint on the accuracy of such estimates at this stage of concept development is the limited amount of high fidelity design and failure information available on the actual system under development. Applying the human ability to learn from experience and augment our state of knowledge to evolve better solutions mitigates this limitation. However, the challenge lies in formalizing a methodology that takes this highly abstract, but fundamentally human cognitive, ability and extending it to the field of risk analysis while maintaining the tenets of generalization, Bayesian inference, and probabilistic risk analysis. We introduce an integrated framework for inferring the reliability, or other probabilistic measures of interest, of a new system or a conceptual variant of an existing system. Abstractly, our framework is based on learning from the performance of precedent designs and then applying the acquired knowledge, appropriately adjusted based on degree of relevance, to the inference process. This dissertation presents a method for inferring properties of the conceptual variant using a pseudo-spatial model that describes the spatial configuration of the family of systems to which the concept belongs. Through non-metric multidimensional scaling, we formulate the pseudo-spatial model based on rank-ordered subjective expert perception of design similarity between systems that elucidate the psychological space of the family. By a novel extension of Kriging methods for analysis of geospatial data to our "pseudo-space of comparable engineered systems", we develop a Bayesian inference model that allows prediction of the probabilistic measure of interest.

Flow and active mixing have a strong impact on bacterial growth dynamics in the proximal large intestine

Science.gov (United States)

Cremer, Jonas; Segota, Igor; Yang, Chih-Yu; Arnoldini, Markus; Groisman, Alex; Hwa, Terence

2016-11-01

More than half of fecal dry weight is bacterial mass with bacterial densities reaching up to 1012 cells per gram. Mostly, these bacteria grow in the proximal large intestine where lateral flow along the intestine is strong: flow can in principal lead to a washout of bacteria from the proximal large intestine. Active mixing by contractions of the intestinal wall together with bacterial growth might counteract such a washout and allow high bacterial densities to occur. As a step towards understanding bacterial growth in the presence of mixing and flow, we constructed an in-vitro setup where controlled wall-deformations of a channel emulate contractions. We investigate growth along the channel under a steady nutrient inflow. Depending on mixing and flow, we observe varying spatial gradients in bacterial density along the channel. Active mixing by deformations of the channel wall is shown to be crucial in maintaining a steady-state bacterial population in the presence of flow. The growth-dynamics is quantitatively captured by a simple mathematical model, with the effect of mixing described by an effective diffusion term. Based on this model, we discuss bacterial growth dynamics in the human large intestine using flow- and mixing-behavior having been observed for humans.

On evaluating the robustness of spatial-proximity-based regionalization methods.

OpenAIRE

Lebecherel , L.; Andréassian , V.; Perrin , C.

2016-01-01

International audience; In absence of streamflow data to calibrate a hydrological model, its parameters are to be inferred by a regionalization method. In this technical note, we discuss a specific class of regionalization methods, those based on spatial proximity, which transfers hydrological information (typically calibrated parameter sets) from neighbor gauged stations to the target ungauged station. The efficiency of any spatialproximity-based regionalization method will depend on the den...

On evaluating the robustness of spatial-proximity-based regionalization methods.

OpenAIRE

Lebecherel, L.; Andréassian, V.; Perrin, C.

2016-01-01

In absence of streamflow data to calibrate a hydrological model, its parameters are to be inferred by a regionalization method. In this technical note, we discuss a specific class of regionalization methods, those based on spatial proximity, which transfers hydrological information (typically calibrated parameter sets) from neighbor gauged stations to the target ungauged station. The efficiency of any spatialproximity-based regionalization method will depend on the density of the available st...

Revealing structure and assembly cues for Arabidopsis root-inhabiting bacterial microbiota.

Science.gov (United States)

Bulgarelli, Davide; Rott, Matthias; Schlaeppi, Klaus; Ver Loren van Themaat, Emiel; Ahmadinejad, Nahal; Assenza, Federica; Rauf, Philipp; Huettel, Bruno; Reinhardt, Richard; Schmelzer, Elmon; Peplies, Joerg; Gloeckner, Frank Oliver; Amann, Rudolf; Eickhorst, Thilo; Schulze-Lefert, Paul

2012-08-02

The plant root defines the interface between a multicellular eukaryote and soil, one of the richest microbial ecosystems on Earth. Notably, soil bacteria are able to multiply inside roots as benign endophytes and modulate plant growth and development, with implications ranging from enhanced crop productivity to phytoremediation. Endophytic colonization represents an apparent paradox of plant innate immunity because plant cells can detect an array of microbe-associated molecular patterns (also known as MAMPs) to initiate immune responses to terminate microbial multiplication. Several studies attempted to describe the structure of bacterial root endophytes; however, different sampling protocols and low-resolution profiling methods make it difficult to infer general principles. Here we describe methodology to characterize and compare soil- and root-inhabiting bacterial communities, which reveals not only a function for metabolically active plant cells but also for inert cell-wall features in the selection of soil bacteria for host colonization. We show that the roots of Arabidopsis thaliana, grown in different natural soils under controlled environmental conditions, are preferentially colonized by Proteobacteria, Bacteroidetes and Actinobacteria, and each bacterial phylum is represented by a dominating class or family. Soil type defines the composition of root-inhabiting bacterial communities and host genotype determines their ribotype profiles to a limited extent. The identification of soil-type-specific members within the root-inhabiting assemblies supports our conclusion that these represent soil-derived root endophytes. Surprisingly, plant cell-wall features of other tested plant species seem to provide a sufficient cue for the assembly of approximately 40% of the Arabidopsis bacterial root-inhabiting microbiota, with a bias for Betaproteobacteria. Thus, this root sub-community may not be Arabidopsis-specific but saprophytic bacteria that would naturally be found

Cooperation in carbon source degradation shapes spatial self-organization of microbial consortia on hydrated surfaces

OpenAIRE

Tecon, Robin; Or, Dani

2017-01-01

Mounting evidence suggests that natural microbial communities exhibit a high level of spatial organization at the micrometric scale that facilitate ecological interactions and support biogeochemical cycles. Microbial patterns are difficult to study definitively in natural environments due to complex biodiversity, observability and variable physicochemical factors. Here, we examine how trophic dependencies give rise to self-organized spatial patterns of a well-defined bacterial consortium grow...

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.

Spatial Learning: Conditions and Basic Effects

Directory of Open Access Journals (Sweden)

Victoria D. Chamizo

2002-01-01

Full Text Available A growing body of evidence suggests that the spatial and the temporal domains seem to share the same or similar conditions, basic effects, and mechanisms. The blocking, unblocking and overshadowing experiments (and also those of latent inhibition and perceptual learning reviewed by Prados and Redhead in this issue show that to exclude associative learning as a basic mechanism responsible for spatial learning is quite inappropriate. All these results, especially those obtained with strictly spatial tasks, seem inconsistent with O’Keefe and Nadel’s account of true spatial learning or locale learning. Their theory claims that this kind of learning is fundamentally different and develops with total independence from other ways of learning (like classical and instrumental conditioning -taxon learning. In fact, the results reviewed can be explained appealing on to a sophisticated guidance system, like for example the one proposed by Leonard and McNaughton (1990; see also McNaughton and cols, 1996. Such a system would allow that an animal generates new space information: given the distance and address from of A to B and from A to C, being able to infer the distance and the address from B to C, even when C is invisible from B (see Chapuis and Varlet, 1987 -the contribution by McLaren in this issue constitutes a good example of a sophisticated guidance system.

Bacterial community of cushion plant Thylacospermum ceaspitosum on elevational gradient in the Himalayan cold desert.

Science.gov (United States)

Řeháková, Klára; Chroňáková, Alica; Krištůfek, Václav; Kuchtová, Barbora; Čapková, Kateřina; Scharfen, Josef; Čapek, Petr; Doležal, Jiří

2015-01-01

Although bacterial assemblages are important components of soils in arid ecosystems, the knowledge about composition, life-strategies, and environmental drivers is still fragmentary, especially in remote high-elevation mountains. We compared the quality and quantity of heterotrophic bacterial assemblages between the rhizosphere of the dominant cushion-forming plant Thylacospermum ceaspitosum and its surrounding bulk soil in two mountain ranges (East Karakoram: 4850-5250 m and Little Tibet: 5350-5850 m), in communities from cold steppes to the subnival zone in Ladakh, arid Trans-Himalaya, northwest India. Bacterial communities were characterized by molecular fingerprinting in combination with culture-dependent methods. The effects of environmental factors (elevation, mountain range, and soil physico-chemical parameters) on the bacterial community composition and structure were tested by multivariate redundancy analysis and conditional inference trees. Actinobacteria dominate the cultivable part of community and represent a major bacterial lineage of cold desert soils. The most abundant genera were Streptomyces, Arthrobacter, and Paenibacillus, representing both r- and K-strategists. The soil texture is the most important factor for the community structure and the total bacteria counts. Less abundant and diverse assemblages are found in East Karakoram with coarser soils derived from leucogranite bedrock, while more diverse assemblages in Little Tibet are associated with finer soils derived from easily weathering gneisses. Cushion rhizosphere is in general less diverse than bulk soil, and contains more r-strategists. K-strategists are more associated with the extremes of the gradient, with drought at lowest elevations (4850-5000 m) and frost at the highest elevations (5750-5850 m). The present study illuminates the composition of soil bacterial assemblages in relation to the cushion plant T. ceaspitosum in a xeric environment and brings important information about

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.

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...

Bayesian electron density inference from JET lithium beam emission spectra using Gaussian processes

Science.gov (United States)

Kwak, Sehyun; Svensson, J.; Brix, M.; Ghim, Y.-C.; Contributors, JET

2017-03-01

A Bayesian model to infer edge electron density profiles is developed for the JET lithium beam emission spectroscopy (Li-BES) system, measuring Li I (2p-2s) line radiation using 26 channels with  ∼1 cm spatial resolution and 10∼ 20 ms temporal resolution. The density profile is modelled using a Gaussian process prior, and the uncertainty of the density profile is calculated by a Markov Chain Monte Carlo (MCMC) scheme. From the spectra measured by the transmission grating spectrometer, the Li I line intensities are extracted, and modelled as a function of the plasma density by a multi-state model which describes the relevant processes between neutral lithium beam atoms and plasma particles. The spectral model fully takes into account interference filter and instrument effects, that are separately estimated, again using Gaussian processes. The line intensities are inferred based on a spectral model consistent with the measured spectra within their uncertainties, which includes photon statistics and electronic noise. Our newly developed method to infer JET edge electron density profiles has the following advantages in comparison to the conventional method: (i) providing full posterior distributions of edge density profiles, including their associated uncertainties, (ii) the available radial range for density profiles is increased to the full observation range (∼26 cm), (iii) an assumption of monotonic electron density profile is not necessary, (iv) the absolute calibration factor of the diagnostic system is automatically estimated overcoming the limitation of the conventional technique and allowing us to infer the electron density profiles for all pulses without preprocessing the data or an additional boundary condition, and (v) since the full spectrum is modelled, the procedure of modulating the beam to measure the background signal is only necessary for the case of overlapping of the Li I line with impurity lines.

Theoretical and Experimental Study of Bacterial Colony Growth in 3D

Science.gov (United States)

Shao, Xinxian; Mugler, Andrew; Nemenman, Ilya

2014-03-01

Bacterial cells growing in liquid culture have been well studied and modeled. However, in nature, bacteria often grow as biofilms or colonies in physically structured habitats. A comprehensive model for population growth in such conditions has not yet been developed. Based on the well-established theory for bacterial growth in liquid culture, we develop a model for colony growth in 3D in which a homogeneous colony of cells locally consume a diffusing nutrient. We predict that colony growth is initially exponential, as in liquid culture, but quickly slows to sub-exponential after nutrient is locally depleted. This prediction is consistent with our experiments performed with E. coli in soft agar. Our model provides a baseline to which studies of complex growth process, such as such as spatially and phenotypically heterogeneous colonies, must be compared.

Spatial extreme value analysis to project extremes of large-scale indicators for severe weather.

Science.gov (United States)

Gilleland, Eric; Brown, Barbara G; Ammann, Caspar M

2013-09-01

Concurrently high values of the maximum potential wind speed of updrafts ( W max ) and 0-6 km wind shear (Shear) have been found to represent conducive environments for severe weather, which subsequently provides a way to study severe weather in future climates. Here, we employ a model for the product of these variables (WmSh) from the National Center for Atmospheric Research/United States National Center for Environmental Prediction reanalysis over North America conditioned on their having extreme energy in the spatial field in order to project the predominant spatial patterns of WmSh. The approach is based on the Heffernan and Tawn conditional extreme value model. Results suggest that this technique estimates the spatial behavior of WmSh well, which allows for exploring possible changes in the patterns over time. While the model enables a method for inferring the uncertainty in the patterns, such analysis is difficult with the currently available inference approach. A variation of the method is also explored to investigate how this type of model might be used to qualitatively understand how the spatial patterns of WmSh correspond to extreme river flow events. A case study for river flows from three rivers in northwestern Tennessee is studied, and it is found that advection of WmSh from the Gulf of Mexico prevails while elsewhere, WmSh is generally very low during such extreme events. © 2013 The Authors. Environmetrics published by JohnWiley & Sons, Ltd.

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

The seven sisters DANCe. III. Projected spatial distribution

Science.gov (United States)

Olivares, J.; Moraux, E.; Sarro, L. M.; Bouy, H.; Berihuete, A.; Barrado, D.; Huelamo, N.; Bertin, E.; Bouvier, J.

2018-04-01

Context. Membership analyses of the DANCe and Tycho + DANCe data sets provide the largest and least contaminated sample of Pleiades candidate members to date. Aims: We aim at reassessing the different proposals for the number surface density of the Pleiades in the light of the new and most complete list of candidate members, and inferring the parameters of the most adequate model. Methods: We compute the Bayesian evidence and Bayes Factors for variations of the classical radial models. These include elliptical symmetry, and luminosity segregation. As a by-product of the model comparison, we obtain posterior distributions for each set of model parameters. Results: We find that the model comparison results depend on the spatial extent of the region used for the analysis. For a circle of 11.5 parsecs around the cluster centre (the most homogeneous and complete region), we find no compelling reason to abandon King's model, although the Generalised King model introduced here has slightly better fitting properties. Furthermore, we find strong evidence against radially symmetric models when compared to the elliptic extensions. Finally, we find that including mass segregation in the form of luminosity segregation in the J band is strongly supported in all our models. Conclusions: We have put the question of the projected spatial distribution of the Pleiades cluster on a solid probabilistic framework, and inferred its properties using the most exhaustive and least contaminated list of Pleiades candidate members available to date. Our results suggest however that this sample may still lack about 20% of the expected number of cluster members. Therefore, this study should be revised when the completeness and homogeneity of the data can be extended beyond the 11.5 parsecs limit. Such a study will allow for more precise determination of the Pleiades spatial distribution, its tidal radius, ellipticity, number of objects and total mass.

Analysis of soil whole- and inner-microaggregate bacterial communities

Energy Technology Data Exchange (ETDEWEB)

Mummey, D L; Stahl, P D [University of Wyoming, Laramie, WY (United States). Dept. of Renewable Resources

2004-07-01

Although soil structure largely determines energy flows and the distribution and composition of soil microhabitats, little is known about how microbial community composition is influenced by soil structural characteristics and organic matter compartmentalization dynamics. A UV irradiation-based procedure was developed to specifically isolate inner-microaggregate microbial communities, thus providing the means to analyze these communities in relation to their environment. Whole- and inner-microaggregate fractions of undisturbed soil and soils reclaimed after disturbance by surface coal mining were analyzed using 16S rDNA terminal restriction fragment polymorphism (T-RFLP) and sequence analyses to determine salient bacterial community structural characteristics. We hypothesized that inner-microaggregate environments select for definable microbial communities and that, due to their sequestered environment, inner-microaggregate communities would not be significantly impacted by disturbance. However, T-RFLP analysis indicated distinct differences between bacterial populations of inner-microaggregates of undisturbed and reclaimed soils. While both undisturbed and reclaimed inner-microaggregate bacterial communities were dominated by Actinobacteria, undisturbed soils contained only Actinobacteridae, while in inner-microaggregates of reclaimed soils Rubrobacteridae predominate. Spatial stratification of division-level lineages within microaggregates was also seen. The fractionation methods employed in this study therefore represent a valuable tool for defining relationships between biodiversity and soil structure.

Millennial-scale ocean acidification and late Quaternary decline of cryptic bacterial crusts in tropical reefs.

Science.gov (United States)

Riding, R; Liang, L; Braga, J C

2014-09-01

Ocean acidification by atmospheric carbon dioxide has increased almost continuously since the last glacial maximum (LGM), 21,000 years ago. It is expected to impair tropical reef development, but effects on reefs at the present day and in the recent past have proved difficult to evaluate. We present evidence that acidification has already significantly reduced the formation of calcified bacterial crusts in tropical reefs. Unlike major reef builders such as coralline algae and corals that more closely control their calcification, bacterial calcification is very sensitive to ambient changes in carbonate chemistry. Bacterial crusts in reef cavities have declined in thickness over the past 14,000 years with largest reduction occurring 12,000-10,000 years ago. We interpret this as an early effect of deglacial ocean acidification on reef calcification and infer that similar crusts were likely to have been thicker when seawater carbonate saturation was increased during earlier glacial intervals, and thinner during interglacials. These changes in crust thickness could have substantially affected reef development over glacial cycles, as rigid crusts significantly strengthen framework and their reduction would have increased the susceptibility of reefs to biological and physical erosion. Bacterial crust decline reveals previously unrecognized millennial-scale acidification effects on tropical reefs. This directs attention to the role of crusts in reef formation and the ability of bioinduced calcification to reflect changes in seawater chemistry. It also provides a long-term context for assessing anticipated anthropogenic effects. © 2014 John Wiley & Sons Ltd.

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

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

Multivariate max-stable spatial processes

KAUST Repository

Genton, Marc G.; Padoan, S. A.; Sang, H.

2015-01-01

Max-stable processes allow the spatial dependence of extremes to be modelled and quantified, so they are widely adopted in applications. For a better understanding of extremes, it may be useful to study several variables simultaneously. To this end, we study the maxima of independent replicates of multivariate processes, both in the Gaussian and Student-t cases. We define a Poisson process construction and introduce multivariate versions of the Smith Gaussian extreme-value, the Schlather extremal-Gaussian and extremal-t, and the Brown–Resnick models. We develop inference for the models based on composite likelihoods. We present results of Monte Carlo simulations and an application to daily maximum wind speed and wind gust.

Multivariate max-stable spatial processes

KAUST Repository

Genton, Marc G.

2015-02-11

Max-stable processes allow the spatial dependence of extremes to be modelled and quantified, so they are widely adopted in applications. For a better understanding of extremes, it may be useful to study several variables simultaneously. To this end, we study the maxima of independent replicates of multivariate processes, both in the Gaussian and Student-t cases. We define a Poisson process construction and introduce multivariate versions of the Smith Gaussian extreme-value, the Schlather extremal-Gaussian and extremal-t, and the Brown–Resnick models. We develop inference for the models based on composite likelihoods. We present results of Monte Carlo simulations and an application to daily maximum wind speed and wind gust.

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.

Integrating spatial and biomass planning for the United States

International Nuclear Information System (INIS)

Wang, Sicong; Wang, Shifeng

2016-01-01

Biomass is low-carbon energy and has tremendous potential as an alternative to fossil fuels. However, the significant role of biomass in future low-carbon energy portfolio depends heavily on its consumption. The paper presents a first attempt to examine the spatial-temporal patterns of biomass consumption in the United States (US), using a novel method-spatial Seemingly Unrelated Regression (SUR) model, in order to strengthen the link between energy planning and spatial planning. In order to obtain the robust parameters of spatial SUR models and estimate the parameters efficiently, an iterative maximum likelihood method, which takes full advantage of the stationary characteristic of maximum likelihood estimation, has been developed. The robust parameters of models can help draw a proper inference for biomass consumption. Then the spatial-temporal patterns of biomass consumption in the US at the state level are investigated using the spatial SUR models with the estimation method developed and data covering the period of 2000–2012. Results show that there are spatial dependences among biomass consumption. The presence of spatial dependence in biomass consumption has informative implications for making sustainable biomass polices. It suggests new efforts to adding a cross-state dimension to state-level energy policy and coordinating some elements of energy policy across states are still needed. In addition, results consistent with classic economic theory further proves the correctness of applying the spatial SUR models to investigate the spatial-temporal patterns of biomass consumption. - Highlights: • A spatial model is suggested as framework to investigate biomass consumption. • A new estimation method is developed to obtain the robust parameters of model. • There are spatial dependences among biomass consumption. • The spatial dependence can contribute to making sustainable biomass policies. • Efforts to adding cross-state dimension to state

Spatial distribution of dust in galaxies from the Integral field unit data

Science.gov (United States)

Zafar, Tayyaba; Sophie Dubber, Andrew Hopkins

2018-01-01

An important characteristic of the dust is it can be used as a tracer of stars (and gas) and tell us about the composition of galaxies. Sub-mm and infrared studies can accurately determine the total dust mass and its spatial distribution in massive, bright galaxies. However, faint and distant galaxies are hampered by resolution to dust spatial dust distribution. In the era of integral-field spectrographs (IFS), Balmer decrement is a useful quantity to infer the spatial extent of the dust in distant and low-mass galaxies. We conducted a study to estimate the spatial distribution of dust using the Sydney-Australian Astronomical Observatory (AAO) Multi-object Integral field spectrograph (SAMI) galaxies. Our methodology is unique to exploit the potential of IFS and using the spatial and spectral information together to study dust in galaxies of various morphological types. The spatial extent and content of dust are compared with the star-formation rate, reddening, and inclination of galaxies. We find a right correlation of dust spatial extent with the star-formation rate. The results also indicate a decrease in dust extent radius from Late Spirals to Early Spirals.

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.

Long-term spatial and temporal microbial community dynamics in a large-scale drinking water distribution system with multiple disinfectant regimes.

Science.gov (United States)

Potgieter, Sarah; Pinto, Ameet; Sigudu, Makhosazana; du Preez, Hein; Ncube, Esper; Venter, Stephanus

2018-08-01

Long-term spatial-temporal investigations of microbial dynamics in full-scale drinking water distribution systems are scarce. These investigations can reveal the process, infrastructure, and environmental factors that influence the microbial community, offering opportunities to re-think microbial management in drinking water systems. Often, these insights are missed or are unreliable in short-term studies, which are impacted by stochastic variabilities inherent to large full-scale systems. In this two-year study, we investigated the spatial and temporal dynamics of the microbial community in a large, full scale South African drinking water distribution system that uses three successive disinfection strategies (i.e. chlorination, chloramination and hypochlorination). Monthly bulk water samples were collected from the outlet of the treatment plant and from 17 points in the distribution system spanning nearly 150 km and the bacterial community composition was characterised by Illumina MiSeq sequencing of the V4 hypervariable region of the 16S rRNA gene. Like previous studies, Alpha- and Betaproteobacteria dominated the drinking water bacterial communities, with an increase in Betaproteobacteria post-chloramination. In contrast with previous reports, the observed richness, diversity, and evenness of the bacterial communities were higher in the winter months as opposed to the summer months in this study. In addition to temperature effects, the seasonal variations were also likely to be influenced by changes in average water age in the distribution system and corresponding changes in disinfectant residual concentrations. Spatial dynamics of the bacterial communities indicated distance decay, with bacterial communities becoming increasingly dissimilar with increasing distance between sampling locations. These spatial effects dampened the temporal changes in the bulk water community and were the dominant factor when considering the entire distribution system. However

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.

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.

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.

Active Inference, homeostatic regulation and adaptive behavioural control.

Science.gov (United States)

Pezzulo, Giovanni; Rigoli, Francesco; Friston, Karl

2015-11-01

We review a theory of homeostatic regulation and adaptive behavioural control within the Active Inference framework. Our aim is to connect two research streams that are usually considered independently; namely, Active Inference and associative learning theories of animal behaviour. The former uses a probabilistic (Bayesian) formulation of perception and action, while the latter calls on multiple (Pavlovian, habitual, goal-directed) processes for homeostatic and behavioural control. We offer a synthesis these classical processes and cast them as successive hierarchical contextualisations of sensorimotor constructs, using the generative models that underpin Active Inference. This dissolves any apparent mechanistic distinction between the optimization processes that mediate classical control or learning. Furthermore, we generalize the scope of Active Inference by emphasizing interoceptive inference and homeostatic regulation. The ensuing homeostatic (or allostatic) perspective provides an intuitive explanation for how priors act as drives or goals to enslave action, and emphasises the embodied nature of inference. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

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…

Comparing spatial diversification and meta-population models in the Indo-Australian Archipelago.

Science.gov (United States)

Chalmandrier, Loïc; Albouy, Camille; Descombes, Patrice; Sandel, Brody; Faurby, Soren; Svenning, Jens-Christian; Zimmermann, Niklaus E; Pellissier, Loïc

2018-03-01

Reconstructing the processes that have shaped the emergence of biodiversity gradients is critical to understand the dynamics of diversification of life on Earth. Islands have traditionally been used as model systems to unravel the processes shaping biological diversity. MacArthur and Wilson's island biogeographic model predicts diversity to be based on dynamic interactions between colonization and extinction rates, while treating islands themselves as geologically static entities. The current spatial configuration of islands should influence meta-population dynamics, but long-term geological changes within archipelagos are also expected to have shaped island biodiversity, in part by driving diversification. Here, we compare two mechanistic models providing inferences on species richness at a biogeographic scale: a mechanistic spatial-temporal model of species diversification and a spatial meta-population model. While the meta-population model operates over a static landscape, the diversification model is driven by changes in the size and spatial configuration of islands through time. We compare the inferences of both models to floristic diversity patterns among land patches of the Indo-Australian Archipelago. Simulation results from the diversification model better matched observed diversity than a meta-population model constrained only by the contemporary landscape. The diversification model suggests that the dynamic re-positioning of islands promoting land disconnection and reconnection induced an accumulation of particularly high species diversity on Borneo, which is central within the island network. By contrast, the meta-population model predicts a higher diversity on the mainlands, which is less compatible with empirical data. Our analyses highlight that, by comparing models with contrasting assumptions, we can pinpoint the processes that are most compatible with extant biodiversity patterns.

Comparing spatial diversification and meta-population models in the Indo-Australian Archipelago

Science.gov (United States)

Chalmandrier, Loïc; Albouy, Camille; Descombes, Patrice; Sandel, Brody; Faurby, Soren; Svenning, Jens-Christian; Zimmermann, Niklaus E.

2018-01-01

Reconstructing the processes that have shaped the emergence of biodiversity gradients is critical to understand the dynamics of diversification of life on Earth. Islands have traditionally been used as model systems to unravel the processes shaping biological diversity. MacArthur and Wilson's island biogeographic model predicts diversity to be based on dynamic interactions between colonization and extinction rates, while treating islands themselves as geologically static entities. The current spatial configuration of islands should influence meta-population dynamics, but long-term geological changes within archipelagos are also expected to have shaped island biodiversity, in part by driving diversification. Here, we compare two mechanistic models providing inferences on species richness at a biogeographic scale: a mechanistic spatial-temporal model of species diversification and a spatial meta-population model. While the meta-population model operates over a static landscape, the diversification model is driven by changes in the size and spatial configuration of islands through time. We compare the inferences of both models to floristic diversity patterns among land patches of the Indo-Australian Archipelago. Simulation results from the diversification model better matched observed diversity than a meta-population model constrained only by the contemporary landscape. The diversification model suggests that the dynamic re-positioning of islands promoting land disconnection and reconnection induced an accumulation of particularly high species diversity on Borneo, which is central within the island network. By contrast, the meta-population model predicts a higher diversity on the mainlands, which is less compatible with empirical data. Our analyses highlight that, by comparing models with contrasting assumptions, we can pinpoint the processes that are most compatible with extant biodiversity patterns. PMID:29657753

Functional diversity and redundancy across fish gut, sediment and water bacterial communities.

Science.gov (United States)

Escalas, Arthur; Troussellier, Marc; Yuan, Tong; Bouvier, Thierry; Bouvier, Corinne; Mouchet, Maud A; Flores Hernandez, Domingo; Ramos Miranda, Julia; Zhou, Jizhong; Mouillot, David

2017-08-01

This article explores the functional diversity and redundancy in a bacterial metacommunity constituted of three habitats (sediment, water column and fish gut) in a coastal lagoon under anthropogenic pressure. Comprehensive functional gene arrays covering a wide range of ecological processes and stress resistance genes to estimate the functional potential of bacterial communities were used. Then, diversity partitioning was used to characterize functional diversity and redundancy within (α), between (β) and across (γ) habitats. It was showed that all local communities exhibit a highly diversified potential for the realization of key ecological processes and resistance to various environmental conditions, supporting the growing evidence that macro-organisms microbiomes harbour a high functional potential and are integral components of functional gene dynamics in aquatic bacterial metacommunities. Several levels of functional redundancy at different scales of the bacterial metacommunity were observed (within local communities, within habitats and at the metacommunity level). The results suggested a high potential for the realization of spatial ecological insurance within this ecosystem, that is, the functional compensation among microorganisms for the realization and maintenance of key ecological processes, within and across habitats. Finally, the role of macro-organisms as dispersal vectors of microbes and their potential influence on marine metacommunity dynamics were discussed. © 2017 Society for Applied Microbiology and John Wiley & Sons Ltd.

Biogeography of cryoconite bacterial communities on glaciers of the Tibetan Plateau.

Science.gov (United States)

Liu, Yongqin; Vick-Majors, Trista J; Priscu, John C; Yao, Tandong; Kang, Shichang; Liu, Keshao; Cong, Ziyuang; Xiong, Jingbo; Li, Yang

2017-06-01

Cryoconite holes, water-filled pockets containing biological and mineralogical deposits that form on glacier surfaces, play important roles in glacier mass balance, glacial geochemistry and carbon cycling. The presence of cryoconite material decreases surface albedo and accelerates glacier mass loss, a problem of particular importance in the rapidly melting Tibetan Plateau. No studies have addressed the microbial community composition of cryoconite holes and their associated ecosystem processes on Tibetan glaciers. To further enhance our understanding of these glacial ecosystems on the Tibetan Plateau and to examine their role in carbon cycling as the glaciers respond to climate change, we explored the bacterial communities within cryoconite holes associated with three climatically distinct Tibetan Plateau glaciers using Illumina sequencing of the V4 region of the 16S rRNA gene. Cryoconite bacterial communities were dominated by Cyanobacteria, Chloroflexi, Betaproteobacteria, Bacteroidetes and Actinobacteria. Cryoconite bacterial community composition varied according to their geographical locations, exhibiting significant differences among glaciers studied. Regional beta diversity was driven by the interaction between geographic distance and environmental variables; the latter contributed more than geographic distance to the variation in cryoconite microbial communities. Our study is the first to describe the regional-scale spatial variability and to identify the factors that drive regional variability of cryoconite bacterial communities on the Tibetan Plateau. © FEMS 2017. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Differential growth responses of soil bacterial taxa to carbon substrates of varying chemical recalcitrance

Energy Technology Data Exchange (ETDEWEB)

Goldfarb, K.C.; Karaoz, U.; Hanson, C.A.; Santee, C.A.; Bradford, M.A.; Treseder, K.K.; Wallenstein, M.D.; Brodie, E.L.

2011-04-18

Soils are immensely diverse microbial habitats with thousands of co-existing bacterial, archaeal, and fungal species. Across broad spatial scales, factors such as pH and soil moisture appear to determine the diversity and structure of soil bacterial communities. Within any one site however, bacterial taxon diversity is high and factors maintaining this diversity are poorly resolved. Candidate factors include organic substrate availability and chemical recalcitrance, and given that they appear to structure bacterial communities at the phylum level, we examine whether these factors might structure bacterial communities at finer levels of taxonomic resolution. Analyzing 16S rRNA gene composition of nucleotide analog-labeled DNA by PhyloChip microarrays, we compare relative growth rates on organic substrates of increasing chemical recalcitrance of >2,200 bacterial taxa across 43 divisions/phyla. Taxa that increase in relative abundance with labile organic substrates (i.e., glycine, sucrose) are numerous (>500), phylogenetically clustered, and occur predominantly in two phyla (Proteobacteria and Actinobacteria) including orders Actinomycetales, Enterobacteriales, Burkholderiales, Rhodocyclales, Alteromonadales, and Pseudomonadales. Taxa increasing in relative abundance with more chemically recalcitrant substrates (i.e., cellulose, lignin, or tannin-protein) are fewer (168) but more phylogenetically dispersed, occurring across eight phyla and including Clostridiales, Sphingomonadalaes, Desulfovibrionales. Just over 6% of detected taxa, including many Burkholderiales increase in relative abundance with both labile and chemically recalcitrant substrates. Estimates of median rRNA copy number per genome of responding taxa demonstrate that these patterns are broadly consistent with bacterial growth strategies. Taken together, these data suggest that changes in availability of intrinsically labile substrates may result in predictable shifts in soil bacterial composition.

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.

Implementations of geographically weighted lasso in spatial data with multicollinearity (Case study: Poverty modeling of Java Island)

Science.gov (United States)

Setiyorini, Anis; Suprijadi, Jadi; Handoko, Budhi

2017-03-01

Geographically Weighted Regression (GWR) is a regression model that takes into account the spatial heterogeneity effect. In the application of the GWR, inference on regression coefficients is often of interest, as is estimation and prediction of the response variable. Empirical research and studies have demonstrated that local correlation between explanatory variables can lead to estimated regression coefficients in GWR that are strongly correlated, a condition named multicollinearity. It later results on a large standard error on estimated regression coefficients, and, hence, problematic for inference on relationships between variables. Geographically Weighted Lasso (GWL) is a method which capable to deal with spatial heterogeneity and local multicollinearity in spatial data sets. GWL is a further development of GWR method, which adds a LASSO (Least Absolute Shrinkage and Selection Operator) constraint in parameter estimation. In this study, GWL will be applied by using fixed exponential kernel weights matrix to establish a poverty modeling of Java Island, Indonesia. The results of applying the GWL to poverty datasets show that this method stabilizes regression coefficients in the presence of multicollinearity and produces lower prediction and estimation error of the response variable than GWR does.

The hidden-Markov brain: comparison and inference of white matter hyperintensities on magnetic resonance imaging (MRI)

Science.gov (United States)

Pham, Tuan D.; Salvetti, Federica; Wang, Bing; Diani, Marco; Heindel, Walter; Knecht, Stefan; Wersching, Heike; Baune, Bernhard T.; Berger, Klaus

2011-02-01

Rating and quantification of cerebral white matter hyperintensities on magnetic resonance imaging (MRI) are important tasks in various clinical and scientific settings. As manual evaluation is time consuming and imprecise, much effort has been made to automate the quantification of white matter hyperintensities. There is rarely any report that attempts to study the similarity/dissimilarity of white matter hyperintensity patterns that have different sizes, shapes and spatial localizations on the MRI. This paper proposes an original computational neuroscience framework for such a conceptual study with a standpoint that the prior knowledge about white matter hyperintensities can be accumulated and utilized to enable a reliable inference of the rating of a new white matter hyperintensity observation. This computational approach for rating inference of white matter hyperintensities, which appears to be the first study, can be utilized as a computerized rating-assisting tool and can be very economical for diagnostic evaluation of brain tissue lesions.

Parametric statistical inference basic theory and modern approaches

CERN Document Server

Zacks, Shelemyahu; Tsokos, C P

1981-01-01

Parametric Statistical Inference: Basic Theory and Modern Approaches presents the developments and modern trends in statistical inference to students who do not have advanced mathematical and statistical preparation. The topics discussed in the book are basic and common to many fields of statistical inference and thus serve as a jumping board for in-depth study. The book is organized into eight chapters. Chapter 1 provides an overview of how the theory of statistical inference is presented in subsequent chapters. Chapter 2 briefly discusses statistical distributions and their properties. Chapt

Bacterial mycophagy: definition and diagnosis of a unique bacterial-fungal interaction

NARCIS (Netherlands)

Leveau, J.H.J.; Preston, G.M.

2008-01-01

This review analyses the phenomenon of bacterial mycophagy, which we define as a set of phenotypic behaviours that enable bacteria to obtain nutrients from living fungi and thus allow the conversion of fungal into bacterial biomass. We recognize three types of bacterial strategies to derive

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.

Spatial distribution of enzyme driven reactions at micro-scales

Science.gov (United States)

Kandeler, Ellen; Boeddinghaus, Runa; Nassal, Dinah; Preusser, Sebastian; Marhan, Sven; Poll, Christian

2017-04-01

Studies of microbial biogeography can often provide key insights into the physiologies, environmental tolerances, and ecological strategies of soil microorganisms that dominate in natural environments. In comparison with aquatic systems, soils are particularly heterogeneous. Soil heterogeneity results from the interaction of a hierarchical series of interrelated variables that fluctuate at many different spatial and temporal scales. Whereas spatial dependence of chemical and physical soil properties is well known at scales ranging from decimetres to several hundred metres, the spatial structure of soil enzymes is less clear. Previous work has primarily focused on spatial heterogeneity at a single analytical scale using the distribution of individual cells, specific types of organisms or collective parameters such as bacterial abundance or total microbial biomass. There are fewer studies that have considered variations in community function and soil enzyme activities. This presentation will give an overview about recent studies focusing on spatial pattern of different soil enzymes in the terrestrial environment. Whereas zymography allows the visualization of enzyme pattern in the close vicinity of roots, micro-sampling strategies followed by MUF analyses clarify micro-scale pattern of enzymes associated to specific microhabitats (micro-aggregates, organo-mineral complexes, subsoil compartments).

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.

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.

Inferring cetacean population densities from the absolute dynamic topography of the ocean in a hierarchical Bayesian framework.

Directory of Open Access Journals (Sweden)

Mario A Pardo

Full Text Available We inferred the population densities of blue whales (Balaenoptera musculus and short-beaked common dolphins (Delphinus delphis in the Northeast Pacific Ocean as functions of the water-column's physical structure by implementing hierarchical models in a Bayesian framework. This approach allowed us to propagate the uncertainty of the field observations into the inference of species-habitat relationships and to generate spatially explicit population density predictions with reduced effects of sampling heterogeneity. Our hypothesis was that the large-scale spatial distributions of these two cetacean species respond primarily to ecological processes resulting from shoaling and outcropping of the pycnocline in regions of wind-forced upwelling and eddy-like circulation. Physically, these processes affect the thermodynamic balance of the water column, decreasing its volume and thus the height of the absolute dynamic topography (ADT. Biologically, they lead to elevated primary productivity and persistent aggregation of low-trophic-level prey. Unlike other remotely sensed variables, ADT provides information about the structure of the entire water column and it is also routinely measured at high spatial-temporal resolution by satellite altimeters with uniform global coverage. Our models provide spatially explicit population density predictions for both species, even in areas where the pycnocline shoals but does not outcrop (e.g. the Costa Rica Dome and the North Equatorial Countercurrent thermocline ridge. Interannual variations in distribution during El Niño anomalies suggest that the population density of both species decreases dramatically in the Equatorial Cold Tongue and the Costa Rica Dome, and that their distributions retract to particular areas that remain productive, such as the more oceanic waters in the central California Current System, the northern Gulf of California, the North Equatorial Countercurrent thermocline ridge, and the more

Phylogenetic and functional diversity of the cultivable bacterial community associated with the paralytic shellfish poisoning dinoflagellate Gymnodinium catenatum.

Science.gov (United States)

Green, David H; Llewellyn, Lyndon E; Negri, Andrew P; Blackburn, Susan I; Bolch, Christopher J S

2004-03-01

Gymnodinium catenatum is one of several dinoflagellates that produce a suite of neurotoxins called the paralytic shellfish toxins (PST), responsible for outbreaks of paralytic shellfish poisoning in temperate and tropical waters. Previous research suggested that the bacteria associated with the surface of the sexual resting stages (cyst) were important to the production of PST by G. catenatum. This study sought to characterise the cultivable bacterial diversity of seven different strains of G. catenatum that produce both high and abnormally low amounts of PST, with the long-term aim of understanding the role the bacterial flora has in bloom development and toxicity of this alga. Sixty-one bacterial isolates were cultured and phylogenetically identified as belonging to the Proteobacteria (70%), Bacteroidetes (26%) or Actinobacteria (3%). The Alphaproteobacteria were the most numerous both in terms of the number of isolates cultured (49%) and were also the most abundant type of bacteria in each G. catenatum culture. Two phenotypic (functional) traits inferred from the phylogenetic data were shown to be a common feature of the bacteria present in each G. catenatum culture: firstly, Alphaproteobacteria capable of aerobic anoxygenic photosynthesis, and secondly, Gammaproteobacteria capable of hydrocarbon utilisation and oligotrophic growth. In relation to reports of autonomous production of PST by dinoflagellate-associated bacteria, PST production by bacterial isolates was investigated, but none were shown to produce any PST-like toxins. Overall, this study has identified a number of emergent trends in the bacterial community of G. catenatum which are mirrored in the bacterial flora of other dinoflagellates, and that are likely to be of especial relevance to the population dynamics of natural and harmful algal blooms.

Ensemble stacking mitigates biases in inference of synaptic connectivity

Directory of Open Access Journals (Sweden)

Brendan Chambers

2018-03-01

Full Text Available A promising alternative to directly measuring the anatomical connections in a neuronal population is inferring the connections from the activity. We employ simulated spiking neuronal networks to compare and contrast commonly used inference methods that identify likely excitatory synaptic connections using statistical regularities in spike timing. We find that simple adjustments to standard algorithms improve inference accuracy: A signing procedure improves the power of unsigned mutual-information-based approaches and a correction that accounts for differences in mean and variance of background timing relationships, such as those expected to be induced by heterogeneous firing rates, increases the sensitivity of frequency-based methods. We also find that different inference methods reveal distinct subsets of the synaptic network and each method exhibits different biases in the accurate detection of reciprocity and local clustering. To correct for errors and biases specific to single inference algorithms, we combine methods into an ensemble. Ensemble predictions, generated as a linear combination of multiple inference algorithms, are more sensitive than the best individual measures alone, and are more faithful to ground-truth statistics of connectivity, mitigating biases specific to single inference methods. These weightings generalize across simulated datasets, emphasizing the potential for the broad utility of ensemble-based approaches. Mapping the routing of spikes through local circuitry is crucial for understanding neocortical computation. Under appropriate experimental conditions, these maps can be used to infer likely patterns of synaptic recruitment, linking activity to underlying anatomical connections. Such inferences help to reveal the synaptic implementation of population dynamics and computation. We compare a number of standard functional measures to infer underlying connectivity. We find that regularization impacts measures

Constraint Satisfaction Inference : Non-probabilistic Global Inference for Sequence Labelling

NARCIS (Netherlands)

Canisius, S.V.M.; van den Bosch, A.; Daelemans, W.; Basili, R.; Moschitti, A.

2006-01-01

We present a new method for performing sequence labelling based on the idea of using a machine-learning classifier to generate several possible output sequences, and then applying an inference procedure to select the best sequence among those. Most sequence labelling methods following a similar

Novel Perspectives on the Characterization of Species-Dependent Optical Signatures of Bacterial Colonies by Digital Holography.

Directory of Open Access Journals (Sweden)

Igor Buzalewicz

Full Text Available The use of light diffraction for the microbiological diagnosis of bacterial colonies was a significant breakthrough with widespread implications for the food industry and clinical practice. We previously confirmed that optical sensors for bacterial colony light diffraction can be used for bacterial identification. This paper is focused on the novel perspectives of this method based on digital in-line holography (DIH, which is able to reconstruct the amplitude and phase properties of examined objects, as well as the amplitude and phase patterns of the optical field scattered/diffracted by the bacterial colony in any chosen observation plane behind the object from single digital hologram. Analysis of the amplitude and phase patterns inside a colony revealed its unique optical properties, which are associated with the internal structure and geometry of the bacterial colony. Moreover, on a computational level, it is possible to select the desired scattered/diffracted pattern within the entire observation volume that exhibits the largest amount of unique, differentiating bacterial features. These properties distinguish this method from the already proposed sensing techniques based on light diffraction/scattering of bacterial colonies. The reconstructed diffraction patterns have a similar spatial distribution as the recorded Fresnel patterns, previously applied for bacterial identification with over 98% accuracy, but they are characterized by both intensity and phase distributions. Our results using digital holography provide new optical discriminators of bacterial species revealed in one single step in form of new optical signatures of bacterial colonies: digital holograms, reconstructed amplitude and phase patterns, as well as diffraction patterns from all observation space, which exhibit species-dependent features. To the best of our knowledge, this is the first report on bacterial colony analysis via digital holography and our study represents an

Novel Perspectives on the Characterization of Species-Dependent Optical Signatures of Bacterial Colonies by Digital Holography.

Science.gov (United States)

Buzalewicz, Igor; Kujawińska, Małgorzata; Krauze, Wojciech; Podbielska, Halina

2016-01-01

The use of light diffraction for the microbiological diagnosis of bacterial colonies was a significant breakthrough with widespread implications for the food industry and clinical practice. We previously confirmed that optical sensors for bacterial colony light diffraction can be used for bacterial identification. This paper is focused on the novel perspectives of this method based on digital in-line holography (DIH), which is able to reconstruct the amplitude and phase properties of examined objects, as well as the amplitude and phase patterns of the optical field scattered/diffracted by the bacterial colony in any chosen observation plane behind the object from single digital hologram. Analysis of the amplitude and phase patterns inside a colony revealed its unique optical properties, which are associated with the internal structure and geometry of the bacterial colony. Moreover, on a computational level, it is possible to select the desired scattered/diffracted pattern within the entire observation volume that exhibits the largest amount of unique, differentiating bacterial features. These properties distinguish this method from the already proposed sensing techniques based on light diffraction/scattering of bacterial colonies. The reconstructed diffraction patterns have a similar spatial distribution as the recorded Fresnel patterns, previously applied for bacterial identification with over 98% accuracy, but they are characterized by both intensity and phase distributions. Our results using digital holography provide new optical discriminators of bacterial species revealed in one single step in form of new optical signatures of bacterial colonies: digital holograms, reconstructed amplitude and phase patterns, as well as diffraction patterns from all observation space, which exhibit species-dependent features. To the best of our knowledge, this is the first report on bacterial colony analysis via digital holography and our study represents an innovative approach

Reasoning about Informal Statistical Inference: One Statistician's View

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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…

A full scale approximation of covariance functions for large spatial data sets

KAUST Repository

Sang, Huiyan

2011-10-10

Gaussian process models have been widely used in spatial statistics but face tremendous computational challenges for very large data sets. The model fitting and spatial prediction of such models typically require O(n 3) operations for a data set of size n. Various approximations of the covariance functions have been introduced to reduce the computational cost. However, most existing approximations cannot simultaneously capture both the large- and the small-scale spatial dependence. A new approximation scheme is developed to provide a high quality approximation to the covariance function at both the large and the small spatial scales. The new approximation is the summation of two parts: a reduced rank covariance and a compactly supported covariance obtained by tapering the covariance of the residual of the reduced rank approximation. Whereas the former part mainly captures the large-scale spatial variation, the latter part captures the small-scale, local variation that is unexplained by the former part. By combining the reduced rank representation and sparse matrix techniques, our approach allows for efficient computation for maximum likelihood estimation, spatial prediction and Bayesian inference. We illustrate the new approach with simulated and real data sets. © 2011 Royal Statistical Society.

A full scale approximation of covariance functions for large spatial data sets

KAUST Repository

Sang, Huiyan; Huang, Jianhua Z.

2011-01-01

Gaussian process models have been widely used in spatial statistics but face tremendous computational challenges for very large data sets. The model fitting and spatial prediction of such models typically require O(n 3) operations for a data set of size n. Various approximations of the covariance functions have been introduced to reduce the computational cost. However, most existing approximations cannot simultaneously capture both the large- and the small-scale spatial dependence. A new approximation scheme is developed to provide a high quality approximation to the covariance function at both the large and the small spatial scales. The new approximation is the summation of two parts: a reduced rank covariance and a compactly supported covariance obtained by tapering the covariance of the residual of the reduced rank approximation. Whereas the former part mainly captures the large-scale spatial variation, the latter part captures the small-scale, local variation that is unexplained by the former part. By combining the reduced rank representation and sparse matrix techniques, our approach allows for efficient computation for maximum likelihood estimation, spatial prediction and Bayesian inference. We illustrate the new approach with simulated and real data sets. © 2011 Royal Statistical Society.

Distinct Bacterial Composition Associated with Different Laboratory-cultured Aiptasia Strains Across Two Thermal Conditions

KAUST Repository

Ahmed, Hanin

2018-05-01

saline environments and can tolerate high temperatures. Putative functional profiles based on taxonomic inference of associated bacterial taxa (i.e., enrichment and depletion of various metabolic processes) were also identified, implying functional differences of the microbiomes associated with Aiptasia strains in response to heat stress. Future studies should more specifically examine how the microbiome influences the animal ability to respond to environmental changes.

Social behaviour and decision making in bacterial conjugation

Directory of Open Access Journals (Sweden)

Günther eKoraimann

2014-04-01

Full Text Available Bacteria frequently acquire novel genes by HGT (horizontal gene transfer. HGT through the process of bacterial conjugation is highly efficient and depends on the presence of conjugative plasmids (CPs or integrated conjugative elements (ICEs that provide the necessary genes for DNA transmission. This review focuses on recent advancements in our understanding of ssDNA transfer systems and regulatory networks ensuring timely and spatially controlled DNA transfer (tra gene expression. As will become obvious by comparing different systems, by default, tra genes are shut off in cells in which conjugative elements are present. Only when conditions are optimal, donor cells – through epigenetic alleviation of negatively acting roadblocks and direct stimulation of DNA transfer genes – become transfer competent. These transfer competent cells have developmentally transformed into specialized cells capable of secreting ssDNA via a T4S (type IV secretion complex directly into recipient cells. Intriguingly, even under optimal conditions, only a fraction of the population undergoes this transition, a finding that indicates specialization and cooperative, social behavior. Thereby, at the population level, the metabolic burden and other negative consequences of tra gene expression are greatly reduced without compromising the ability to horizontally transfer genes to novel bacterial hosts. This undoubtedly intelligent strategy may explain why conjugative elements – CPs and ICEs – have been successfully kept in and evolved with bacteria to constitute a major driving force of bacterial evolution.

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

Statistical inference and Aristotle's Rhetoric.

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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.

msCentipede: Modeling Heterogeneity across Genomic Sites and Replicates Improves Accuracy in the Inference of Transcription Factor Binding.

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Anil Raj

Full Text Available Understanding global gene regulation depends critically on accurate annotation of regulatory elements that are functional in a given cell type. CENTIPEDE, a powerful, probabilistic framework for identifying transcription factor binding sites from tissue-specific DNase I cleavage patterns and genomic sequence content, leverages the hypersensitivity of factor-bound chromatin and the information in the DNase I spatial cleavage profile characteristic of each DNA binding protein to accurately infer functional factor binding sites. However, the model for the spatial profile in this framework fails to account for the substantial variation in the DNase I cleavage profiles across different binding sites. Neither does it account for variation in the profiles at the same binding site across multiple replicate DNase I experiments, which are increasingly available. In this work, we introduce new methods, based on multi-scale models for inhomogeneous Poisson processes, to account for such variation in DNase I cleavage patterns both within and across binding sites. These models account for the spatial structure in the heterogeneity in DNase I cleavage patterns for each factor. Using DNase-seq measurements assayed in a lymphoblastoid cell line, we demonstrate the improved performance of this model for several transcription factors by comparing against the Chip-seq peaks for those factors. Finally, we explore the effects of DNase I sequence bias on inference of factor binding using a simple extension to our framework that allows for a more flexible background model. The proposed model can also be easily applied to paired-end ATAC-seq and DNase-seq data. msCentipede, a Python implementation of our algorithm, is available at http://rajanil.github.io/msCentipede.

msCentipede: Modeling Heterogeneity across Genomic Sites and Replicates Improves Accuracy in the Inference of Transcription Factor Binding.

Science.gov (United States)

Raj, Anil; Shim, Heejung; Gilad, Yoav; Pritchard, Jonathan K; Stephens, Matthew

2015-01-01

Understanding global gene regulation depends critically on accurate annotation of regulatory elements that are functional in a given cell type. CENTIPEDE, a powerful, probabilistic framework for identifying transcription factor binding sites from tissue-specific DNase I cleavage patterns and genomic sequence content, leverages the hypersensitivity of factor-bound chromatin and the information in the DNase I spatial cleavage profile characteristic of each DNA binding protein to accurately infer functional factor binding sites. However, the model for the spatial profile in this framework fails to account for the substantial variation in the DNase I cleavage profiles across different binding sites. Neither does it account for variation in the profiles at the same binding site across multiple replicate DNase I experiments, which are increasingly available. In this work, we introduce new methods, based on multi-scale models for inhomogeneous Poisson processes, to account for such variation in DNase I cleavage patterns both within and across binding sites. These models account for the spatial structure in the heterogeneity in DNase I cleavage patterns for each factor. Using DNase-seq measurements assayed in a lymphoblastoid cell line, we demonstrate the improved performance of this model for several transcription factors by comparing against the Chip-seq peaks for those factors. Finally, we explore the effects of DNase I sequence bias on inference of factor binding using a simple extension to our framework that allows for a more flexible background model. The proposed model can also be easily applied to paired-end ATAC-seq and DNase-seq data. msCentipede, a Python implementation of our algorithm, is available at http://rajanil.github.io/msCentipede.

Children's and adults' judgments of the certainty of deductive inferences, inductive inferences, and guesses.

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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.

Changes in Visual/Spatial and Analytic Strategy Use in Organic Chemistry with the Development of Expertise

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Vlacholia, Maria; Vosniadou, Stella; Roussos, Petros; Salta, Katerina; Kazi, Smaragda; Sigalas, Michael; Tzougraki, Chryssa

2017-01-01

We present two studies that investigated the adoption of visual/spatial and analytic strategies by individuals at different levels of expertise in the area of organic chemistry, using the Visual Analytic Chemistry Task (VACT). The VACT allows the direct detection of analytic strategy use without drawing inferences about underlying mental…

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

Standard Deviation of Spatially-Averaged Surface Cross Section Data from the TRMM Precipitation Radar

Science.gov (United States)

Meneghini, Robert; Jones, Jeffrey A.

2010-01-01

We investigate the spatial variability of the normalized radar cross section of the surface (NRCS or Sigma(sup 0)) derived from measurements of the TRMM Precipitation Radar (PR) for the period from 1998 to 2009. The purpose of the study is to understand the way in which the sample standard deviation of the Sigma(sup 0) data changes as a function of spatial resolution, incidence angle, and surface type (land/ocean). The results have implications regarding the accuracy by which the path integrated attenuation from precipitation can be inferred by the use of surface scattering properties.

Perturbation of seafloor bacterial community structure by drilling waste discharge.

Science.gov (United States)

Nguyen, Tan T; Cochrane, Sabine K J; Landfald, Bjarne

2018-04-01

Offshore drilling operations result in the generation of drill cuttings and localized smothering of the benthic habitats. This study explores bacterial community changes in the in the upper layers of the seafloor resulting from an exploratory drilling operation at 1400m water depth on the Barents Sea continental slope. Significant restructurings of the sediment microbiota were restricted to the sampling sites notably affected by the drilling waste discharge, i.e. at 30m and 50m distances from the drilling location, and to the upper 2cm of the seafloor. Three bacterial groups, the orders Clostridiales and Desulfuromonadales and the class Mollicutes, were almost exclusively confined to the upper two centimeters at 30m distance, thereby corroborating an observed increase in anaerobicity inflicted by the drilling waste deposition. The potential of these phylogenetic groups as microbial bioindicators of the spatial extent and persistence of drilling waste discharge should be further explored. Copyright © 2017 Elsevier Ltd. All rights reserved.

Deep Learning for Population Genetic Inference.

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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.

Modern methodology and applications in spatial-temporal modeling

CERN Document Server

Matsui, Tomoko

2015-01-01

This book provides a modern introductory tutorial on specialized methodological and applied aspects of spatial and temporal modeling. The areas covered involve a range of topics which reflect the diversity of this domain of research across a number of quantitative disciplines. For instance, the first chapter deals with non-parametric Bayesian inference via a recently developed framework known as kernel mean embedding which has had a significant influence in machine learning disciplines. The second chapter takes up non-parametric statistical methods for spatial field reconstruction and exceedance probability estimation based on Gaussian process-based models in the context of wireless sensor network data. The third chapter presents signal-processing methods applied to acoustic mood analysis based on music signal analysis. The fourth chapter covers models that are applicable to time series modeling in the domain of speech and language processing. This includes aspects of factor analysis, independent component an...

A statistical method for lung tumor segmentation uncertainty in PET images based on user inference.

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Zheng, Chaojie; Wang, Xiuying; Feng, Dagan

2015-01-01

PET has been widely accepted as an effective imaging modality for lung tumor diagnosis and treatment. However, standard criteria for delineating tumor boundary from PET are yet to develop largely due to relatively low quality of PET images, uncertain tumor boundary definition, and variety of tumor characteristics. In this paper, we propose a statistical solution to segmentation uncertainty on the basis of user inference. We firstly define the uncertainty segmentation band on the basis of segmentation probability map constructed from Random Walks (RW) algorithm; and then based on the extracted features of the user inference, we use Principle Component Analysis (PCA) to formulate the statistical model for labeling the uncertainty band. We validated our method on 10 lung PET-CT phantom studies from the public RIDER collections [1] and 16 clinical PET studies where tumors were manually delineated by two experienced radiologists. The methods were validated using Dice similarity coefficient (DSC) to measure the spatial volume overlap. Our method achieved an average DSC of 0.878 ± 0.078 on phantom studies and 0.835 ± 0.039 on clinical studies.

Functional Potential of Bacterial Communities using Gene Context Information

Directory of Open Access Journals (Sweden)

Anwesha Mohapatra

2017-12-01

Full Text Available Estimation of the functional potential of a bacterial genome can be determined by accurate annotation of its metabolic pathways. Existing homology based methods for pathway annotation fail to account for homologous genes that participate in multiple pathways, causing overestimation of gene copy number. Mere presence of constituent genes of a candidate pathway which are dispersed on a genome often results in incorrect annotation, thereby leading to erroneous gene abundance and pathway estimation. Clusters of evolutionarily conserved coregulated genes are characteristic features in bacterial genomes and their spatial arrangement in the genome is constrained by the pathway encoded by them. Thus, in order to improve the accuracy of pathway prediction, it is important to augment homology based annotation with gene organization information. In this communication, we present a methodology considering prioritization of gene context for improved pathway annotation. Extensive literature mining was performed to confirm conserved juxtaposed arrangement of gene components of various pathways. Our method was utilized to identify and analyse the functional potential of all available completely sequenced bacterial genomes. The accuracy of the predicted gene clusters and their importance in metabolic pathways will be demonstrated using a few case studies. One of such case study corresponds to butyrate production pathways in gut bacteria where it was observed that gut pathogens and commensals possess a distinct set of pathway components. In another example, we will demonstrate how our methodology improves the prediction accuracy of carbohydrate metabolic potential in human microbial communities. Applicability of our method for estimation of functional potential in bacterial communities present in diverse environments will also be illustrated.

Using Alien Coins to Test Whether Simple Inference Is Bayesian

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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…

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.

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

Exploring the dynamics of bacterial community composition in soil: the pan-bacteriome approach.

Science.gov (United States)

Bacci, Giovanni; Ceccherini, Maria Teresa; Bani, Alessia; Bazzicalupo, Marco; Castaldini, Maurizio; Galardini, Marco; Giovannetti, Luciana; Mocali, Stefano; Pastorelli, Roberta; Pantani, Ottorino Luca; Arfaioli, Paola; Pietramellara, Giacomo; Viti, Carlo; Nannipieri, Paolo; Mengoni, Alessio

2015-03-01

We performed a longitudinal study (repeated observations of the same sample over time) to investigate both the composition and structure of temporal changes of bacterial community composition in soil mesocosms, subjected to three different treatments (water and 5 or 25 mg kg(-1) of dried soil Cd(2+)). By analogy with the pan genome concept, we identified a core bacteriome and an accessory bacteriome. Resident taxa were assigned to the core bacteriome, while occasional taxa were assigned to the accessory bacteriome. Core and accessory bacteriome represented roughly 35 and 50 % of the taxa detected, respectively, and were characterized by different taxonomic signatures from phylum to genus level while 15 % of the taxa were found to be unique to a particular sample. In particular, the core bacteriome was characterized by higher abundance of members of Planctomycetes, Actinobacteria, Verrucomicrobia and Acidobacteria, while the accessory bacteriome included more members of Firmicutes, Clamydiae and Proteobacteria, suggesting potentially different responses to environmental changes of members from these phyla. We conclude that the pan-bacteriome model may be a useful approach to gain insight for modeling bacterial community structure and inferring different abilities of bacteria taxa.

Detecting dynamic causal inference in nonlinear two-phase fracture flow

Science.gov (United States)

Faybishenko, Boris

2017-08-01

Identifying dynamic causal inference involved in flow and transport processes in complex fractured-porous media is generally a challenging task, because nonlinear and chaotic variables may be positively coupled or correlated for some periods of time, but can then become spontaneously decoupled or non-correlated. In his 2002 paper (Faybishenko, 2002), the author performed a nonlinear dynamical and chaotic analysis of time-series data obtained from the fracture flow experiment conducted by Persoff and Pruess (1995), and, based on the visual examination of time series data, hypothesized that the observed pressure oscillations at both inlet and outlet edges of the fracture result from a superposition of both forward and return waves of pressure propagation through the fracture. In the current paper, the author explores an application of a combination of methods for detecting nonlinear chaotic dynamics behavior along with the multivariate Granger Causality (G-causality) time series test. Based on the G-causality test, the author infers that his hypothesis is correct, and presents a causation loop diagram of the spatial-temporal distribution of gas, liquid, and capillary pressures measured at the inlet and outlet of the fracture. The causal modeling approach can be used for the analysis of other hydrological processes, for example, infiltration and pumping tests in heterogeneous subsurface media, and climatic processes, for example, to find correlations between various meteorological parameters, such as temperature, solar radiation, barometric pressure, etc.

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...

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.

Compositional Stability of the Bacterial Community in a Climate-Sensitive Sub-Arctic Peatland.

Science.gov (United States)

Weedon, James T; Kowalchuk, George A; Aerts, Rien; Freriks, Stef; Röling, Wilfred F M; van Bodegom, Peter M

2017-01-01

The climate sensitivity of microbe-mediated soil processes such as carbon and nitrogen cycling offers an interesting case for evaluating the corresponding sensitivity of microbial community composition to environmental change. Better understanding of the degree of linkage between functional and compositional stability would contribute to ongoing efforts to build mechanistic models aiming at predicting rates of microbe-mediated processes. We used an amplicon sequencing approach to test if previously observed large effects of experimental soil warming on C and N cycle fluxes (50-100% increases) in a sub-arctic Sphagnum peatland were reflected in changes in the composition of the soil bacterial community. We found that treatments that previously induced changes to fluxes did not associate with changes in the phylogenetic composition of the soil bacterial community. For both DNA- and RNA-based analyses, variation in bacterial communities could be explained by the hierarchy: spatial variation (12-15% of variance explained) > temporal variation (7-11%) > climate treatment (4-9%). We conclude that the bacterial community in this environment is stable under changing conditions, despite the previously observed sensitivity of process rates-evidence that microbe-mediated soil processes can alter without concomitant changes in bacterial communities. We propose that progress in linking soil microbial communities to ecosystem processes can be advanced by further investigating the relative importance of community composition effects versus physico-chemical factors in controlling biogeochemical process rates in different contexts.

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

Coral Bacterial-Core Abundance and Network Complexity as Proxies for Anthropogenic Pollution

Directory of Open Access Journals (Sweden)

Deborah C. A. Leite

2018-04-01

Full Text Available Acclimatization via changes in the stable (core or the variable microbial diversity and/or abundance is an important element in the adaptation of coral species to environmental changes. Here, we explored the spatial-temporal dynamics, diversity and interactions of variable and core bacterial populations associated with the coral Mussismilia hispida and the surrounding water. This survey was performed on five reefs along a transect from the coast (Reef 1 to offshore (Reef 5, representing a gradient of influence of the river mouth, for almost 12 months (4 sampling times, in the dry and rainy seasons. A clear increasing gradient of organic-pollution proxies (nitrogen content and fecal coliforms was observed from Reef 1 to Reef 5, during both seasons, and was highest at the Buranhém River mouth (Reef 1. Conversely, a clear inverse gradient of the network analysis of the whole bacterial communities also revealed more-complex network relationships at Reef 5. Our data also indicated a higher relative abundance of members of the bacterial core, dominated by Acinetobacter sp., at Reef 5, and higher diversity of site-stable bacterial populations, likely related to the higher abundance of total coliforms and N content (proxies of sewage or organic pollution at Reef 1, during the rainy season. Thus, the less “polluted” areas may show a more-complex network and a high relative abundance of members of the bacterial core (almost 97% in some cases, resulting in a more-homogeneous and well-established bacteriome among sites/samples, when the influence of the river is stronger (rainy seasons.

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

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...

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

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...

Making inference from wildlife collision data: inferring predator absence from prey strikes

Directory of Open Access Journals (Sweden)

Peter Caley

2017-02-01

Full Text Available Wildlife collision data are ubiquitous, though challenging for making ecological inference due to typically irreducible uncertainty relating to the sampling process. We illustrate a new approach that is useful for generating inference from predator data arising from wildlife collisions. By simply conditioning on a second prey species sampled via the same collision process, and by using a biologically realistic numerical response functions, we can produce a coherent numerical response relationship between predator and prey. This relationship can then be used to make inference on the population size of the predator species, including the probability of extinction. The statistical conditioning enables us to account for unmeasured variation in factors influencing the runway strike incidence for individual airports and to enable valid comparisons. A practical application of the approach for testing hypotheses about the distribution and abundance of a predator species is illustrated using the hypothesized red fox incursion into Tasmania, Australia. We estimate that conditional on the numerical response between fox and lagomorph runway strikes on mainland Australia, the predictive probability of observing no runway strikes of foxes in Tasmania after observing 15 lagomorph strikes is 0.001. We conclude there is enough evidence to safely reject the null hypothesis that there is a widespread red fox population in Tasmania at a population density consistent with prey availability. The method is novel and has potential wider application.

Making inference from wildlife collision data: inferring predator absence from prey strikes.

Science.gov (United States)

Caley, Peter; Hosack, Geoffrey R; Barry, Simon C

2017-01-01

Wildlife collision data are ubiquitous, though challenging for making ecological inference due to typically irreducible uncertainty relating to the sampling process. We illustrate a new approach that is useful for generating inference from predator data arising from wildlife collisions. By simply conditioning on a second prey species sampled via the same collision process, and by using a biologically realistic numerical response functions, we can produce a coherent numerical response relationship between predator and prey. This relationship can then be used to make inference on the population size of the predator species, including the probability of extinction. The statistical conditioning enables us to account for unmeasured variation in factors influencing the runway strike incidence for individual airports and to enable valid comparisons. A practical application of the approach for testing hypotheses about the distribution and abundance of a predator species is illustrated using the hypothesized red fox incursion into Tasmania, Australia. We estimate that conditional on the numerical response between fox and lagomorph runway strikes on mainland Australia, the predictive probability of observing no runway strikes of foxes in Tasmania after observing 15 lagomorph strikes is 0.001. We conclude there is enough evidence to safely reject the null hypothesis that there is a widespread red fox population in Tasmania at a population density consistent with prey availability. The method is novel and has potential wider application.

Generalized Spatial Two Stage Least Squares Estimation of Spatial Autoregressive Models with Autoregressive Disturbances in the Presence of Endogenous Regressors and Many Instruments

Directory of Open Access Journals (Sweden)

Fei Jin

2013-05-01

Full Text Available This paper studies the generalized spatial two stage least squares (GS2SLS estimation of spatial autoregressive models with autoregressive disturbances when there are endogenous regressors with many valid instruments. Using many instruments may improve the efficiency of estimators asymptotically, but the bias might be large in finite samples, making the inference inaccurate. We consider the case that the number of instruments K increases with, but at a rate slower than, the sample size, and derive the approximate mean square errors (MSE that account for the trade-offs between the bias and variance, for both the GS2SLS estimator and a bias-corrected GS2SLS estimator. A criterion function for the optimal K selection can be based on the approximate MSEs. Monte Carlo experiments are provided to show the performance of our procedure of choosing K.

Causal inference in biology networks with integrated belief propagation.

Science.gov (United States)

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

2015-01-01

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

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.

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.

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

Systems, methods, and software for determining spatially variable distributions of the dielectric properties of a heterogeneous material

Science.gov (United States)

Farrington, Stephen P.

2018-05-15

Systems, methods, and software for measuring the spatially variable relative dielectric permittivity of materials along a linear or otherwise configured sensor element, and more specifically the spatial variability of soil moisture in one dimension as inferred from the dielectric profile of the soil matrix surrounding a linear sensor element. Various methods provided herein combine advances in the processing of time domain reflectometry data with innovations in physical sensing apparatuses. These advancements enable high temporal (and thus spatial) resolution of electrical reflectance continuously along an insulated waveguide that is permanently emplaced in contact with adjacent soils. The spatially resolved reflectance is directly related to impedance changes along the waveguide that are dominated by electrical permittivity contrast due to variations in soil moisture. Various methods described herein are thus able to monitor soil moisture in profile with high spatial resolution.

Tree phyllosphere bacterial communities: exploring the magnitude of intra- and inter-individual variation among host species

Directory of Open Access Journals (Sweden)

Isabelle Laforest-Lapointe

2016-08-01

Full Text Available Background The diversity and composition of the microbial community of tree leaves (the phyllosphere varies among trees and host species and along spatial, temporal, and environmental gradients. Phyllosphere community variation within the canopy of an individual tree exists but the importance of this variation relative to among-tree and among-species variation is poorly understood. Sampling techniques employed for phyllosphere studies include picking leaves from one canopy location to mixing randomly selected leaves from throughout the canopy. In this context, our goal was to characterize the relative importance of intra-individual variation in phyllosphere communities across multiple species, and compare this variation to inter-individual and interspecific variation of phyllosphere epiphytic bacterial communities in a natural temperate forest in Quebec, Canada. Methods We targeted five dominant temperate forest tree species including angiosperms and gymnosperms: Acer saccharum, Acer rubrum, Betula papyrifera, Abies balsamea and Picea glauca. For one randomly selected tree of each species, we sampled microbial communities at six distinct canopy locations: bottom-canopy (1–2 m height, the four cardinal points of mid-canopy (2–4 m height, and the top-canopy (4–6 m height. We also collected bottom-canopy leaves from five additional trees from each species. Results Based on an analysis of bacterial community structure measured via Illumina sequencing of the bacterial 16S gene, we demonstrate that 65% of the intra-individual variation in leaf bacterial community structure could be attributed to the effect of inter-individual and inter-specific differences while the effect of canopy location was not significant. In comparison, host species identity explains 47% of inter-individual and inter-specific variation in leaf bacterial community structure followed by individual identity (32% and canopy location (6%. Discussion Our results suggest that

Deep Learning for Population Genetic Inference.

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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.

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

Evaluation of Automated Ribosomal Intergenic Spacer Analysis for Bacterial Fingerprinting of Rumen Microbiome Compared to Pyrosequencing Technology

Directory of Open Access Journals (Sweden)

Elie Jami

2014-01-01

Full Text Available The mammalian gut houses a complex microbial community which is believed to play a significant role in host physiology. In recent years, several microbial community analysis methods have been implemented to study the whole gut microbial environment, in contrast to classical microbiological methods focusing on bacteria which can be cultivated. One of these is automated ribosomal intergenic spacer analysis (ARISA, an inexpensive and popular way of analyzing bacterial diversity and community fingerprinting in ecological samples. ARISA uses the natural variability in length of the DNA fragment found between the 16S and 23S genes in different bacterial lineages to infer diversity. This method is now being supplanted by affordable next-generation sequencing technologies that can also simultaneously annotate operational taxonomic units for taxonomic identification. We compared ARISA and pyrosequencing of samples from the rumen microbiome of cows, previously sampled at different stages of development and varying in microbial complexity using several ecological parameters. We revealed close agreement between ARISA and pyrosequencing outputs, especially in their ability to discriminate samples from different ecological niches. In contrast, the ARISA method seemed to underestimate sample richness. The good performance of the relatively inexpensive ARISA makes it relevant for straightforward use in bacterial fingerprinting analysis as well as for quick cross-validation of pyrosequencing data.

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...

Characterization of coastal urban watershed bacterial communities leads to alternative community-based indicators

Energy Technology Data Exchange (ETDEWEB)

Wu, C.H.; Sercu, B.; Van De Werhorst, L.C.; Wong, J.; DeSantis, T.Z.; Brodie, E.L.; Hazen, T.C.; Holden, P.A.; Andersen, G.L.

2010-03-01

Microbial communities in aquatic environments are spatially and temporally dynamic due to environmental fluctuations and varied external input sources. A large percentage of the urban watersheds in the United States are affected by fecal pollution, including human pathogens, thus warranting comprehensive monitoring. Using a high-density microarray (PhyloChip), we examined water column bacterial community DNA extracted from two connecting urban watersheds, elucidating variable and stable bacterial subpopulations over a 3-day period and community composition profiles that were distinct to fecal and non-fecal sources. Two approaches were used for indication of fecal influence. The first approach utilized similarity of 503 operational taxonomic units (OTUs) common to all fecal samples analyzed in this study with the watershed samples as an index of fecal pollution. A majority of the 503 OTUs were found in the phyla Firmicutes, Proteobacteria, Bacteroidetes, and Actinobacteria. The second approach incorporated relative richness of 4 bacterial classes (Bacilli, Bacteroidetes, Clostridia and a-proteobacteria) found to have the highest variance in fecal and non-fecal samples. The ratio of these 4 classes (BBC:A) from the watershed samples demonstrated a trend where bacterial communities from gut and sewage sources had higher ratios than from sources not impacted by fecal material. This trend was also observed in the 124 bacterial communities from previously published and unpublished sequencing or PhyloChip- analyzed studies. This study provided a detailed characterization of bacterial community variability during dry weather across a 3-day period in two urban watersheds. The comparative analysis of watershed community composition resulted in alternative community-based indicators that could be useful for assessing ecosystem health.

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...

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

Topological chaos of the spatial prisoner's dilemma game on regular networks.

Science.gov (United States)

Jin, Weifeng; Chen, Fangyue

2016-02-21

The spatial version of evolutionary prisoner's dilemma on infinitely large regular lattice with purely deterministic strategies and no memories among players is investigated in this paper. Based on the statistical inferences, it is pertinent to confirm that the frequency of cooperation for characterizing its macroscopic behaviors is very sensitive to the initial conditions, which is the most practically significant property of chaos. Its intrinsic complexity is then justified on firm ground from the theory of symbolic dynamics; that is, this game is topologically mixing and possesses positive topological entropy on its subsystems. It is demonstrated therefore that its frequency of cooperation could not be adopted by simply averaging over several steps after the game reaches the equilibrium state. Furthermore, the chaotically changing spatial patterns via empirical observations can be defined and justified in view of symbolic dynamics. It is worth mentioning that the procedure proposed in this work is also applicable to other deterministic spatial evolutionary games therein. Copyright © 2015 Elsevier Ltd. All rights reserved.

Environmental determinants and spatial mismatch of mammal diversity measures in Colombia

Energy Technology Data Exchange (ETDEWEB)

Gonzalez-Maya, J.F.; Arias-Alzate, A.; Granados-Peña, R.; Mancera-Rodriguez, N.J.; Ceballos, G.

2016-07-01

Including complementary diversity measures into ecological and conservation studies should improve our ability to link species assemblages to ecosystems. Recent measures such as phylogenetic and functional diversity have furthered our understanding of assemblage patterns of ecosystems and species, allowing improved inference of ecosystem function and conservation. We evaluated spatial patterns of taxonomic, phylogenetic and functional diversity of mammals in Colombia and identified their main environmental determinants, as well as interrelationships and spatial mismatch between the three measures. We found significant effects of elevation and precipitation on species richness, slope and species richness on phylogenetic diversity, and slope and phylogenetic diversity on functional diversity. We also identified a spatial mismatch of the three measures in some areas of the country: 12% of the country for species richness and 14% for phylogenetic and functional diversity. Our results highlight the importance of including species relationships within environmental drivers with biogeographical and distribution analyses and could facilitate selection of priority areas for conservation, especially when mismatch occurs between measures. (Author)

Visualization of simulated urban spaces: inferring parameterized generation of streets, parcels, and aerial imagery.

Science.gov (United States)

Vanegas, Carlos A; Aliaga, Daniel G; Benes, Bedrich; Waddell, Paul

2009-01-01

Urban simulation models and their visualization are used to help regional planning agencies evaluate alternative transportation investments, land use regulations, and environmental protection policies. Typical urban simulations provide spatially distributed data about number of inhabitants, land prices, traffic, and other variables. In this article, we build on a synergy of urban simulation, urban visualization, and computer graphics to automatically infer an urban layout for any time step of the simulation sequence. In addition to standard visualization tools, our method gathers data of the original street network, parcels, and aerial imagery and uses the available simulation results to infer changes to the original urban layout and produce a new and plausible layout for the simulation results. In contrast with previous work, our approach automatically updates the layout based on changes in the simulation data and thus can scale to a large simulation over many years. The method in this article offers a substantial step forward in building integrated visualization and behavioral simulation systems for use in community visioning, planning, and policy analysis. We demonstrate our method on several real cases using a 200 GB database for a 16,300 km2 area surrounding Seattle.

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

Distinct bacterial communities in surficial seafloor sediments following the 2010 Deepwater Horizon blowout

Directory of Open Access Journals (Sweden)

Tingting Yang

2016-09-01

Full Text Available A major fraction of the petroleum hydrocarbons discharged during the 2010 Macondo oil spill became associated with and sank to the seafloor as marine snow flocs. This sedimentation pulse induced the development of distinct bacterial communities. Between May 2010 and July 2011, full-length 16S rRNA gene clone libraries demonstrated bacterial community succession in oil-polluted sediment samples near the wellhead area. Libraries from early May 2010, before the sedimentation event, served as the baseline control. Freshly deposited oil-derived marine snow was collected on the surface of sediment cores in September 2010, and was characterized by abundantly detected members of the marine Roseobacter cluster within the Alphaproteobacteria. Samples collected in mid-October 2010 closest to the wellhead contained members of the sulfate-reducing, anaerobic bacterial families Desulfobacteraceae and Desulfobulbaceae within the Deltaproteobacteria, suggesting that the oil-derived sedimentation pulse triggered bacterial oxygen consumption and created patchy anaerobic microniches that favored sulfate-reducing bacteria. Phylotypes of the polycyclic aromatic hydrocarbon-degrading genus Cycloclasticus, previously found both in surface oil slicks and the deep hydrocarbon plume, were also found in oil-derived marine snow flocs sedimenting on the seafloor in September 2010, and in surficial sediments collected in October and November 2010, but not in any of the control samples. Due to the relative recalcitrance and stability of polycyclic aromatic compounds, Cycloclasticus represents the most persistent microbial marker of seafloor hydrocarbon deposition that we could identify in this dataset. The bacterial imprint of the DWH oil spill had diminished in late November 2010, when the bacterial communities in oil-impacted sediment samples collected near the Macondo wellhead began to resemble their pre-spill counterparts and spatial controls. Samples collected in summer

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.

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.>.

[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.

Density of founder cells affects spatial pattern formation and cooperation in Bacillus subtilis biofilms.

Science.gov (United States)

van Gestel, Jordi; Weissing, Franz J; Kuipers, Oscar P; Kovács, Akos T

2014-10-01

In nature, most bacteria live in surface-attached sedentary communities known as biofilms. Biofilms are often studied with respect to bacterial interactions. Many cells inhabiting biofilms are assumed to express 'cooperative traits', like the secretion of extracellular polysaccharides (EPS). These traits can enhance biofilm-related properties, such as stress resilience or colony expansion, while being costly to the cells that express them. In well-mixed populations cooperation is difficult to achieve, because non-cooperative individuals can reap the benefits of cooperation without having to pay the costs. The physical process of biofilm growth can, however, result in the spatial segregation of cooperative from non-cooperative individuals. This segregation can prevent non-cooperative cells from exploiting cooperative neighbors. Here we examine the interaction between spatial pattern formation and cooperation in Bacillus subtilis biofilms. We show, experimentally and by mathematical modeling, that the density of cells at the onset of biofilm growth affects pattern formation during biofilm growth. At low initial cell densities, co-cultured strains strongly segregate in space, whereas spatial segregation does not occur at high initial cell densities. As a consequence, EPS-producing cells have a competitive advantage over non-cooperative mutants when biofilms are initiated at a low density of founder cells, whereas EPS-deficient cells have an advantage at high cell densities. These results underline the importance of spatial pattern formation for competition among bacterial strains and the evolution of microbial cooperation.

Sibship reconstruction for inferring mating systems, dispersal and effective population size in headwater brook trout (Salvelinus fontinalis) populations

Science.gov (United States)

Kanno, Yoichiro; Vokoun, Jason C.; Letcher, Benjamin H.

2011-01-01

Brook trout Salvelinus fontinalis populations have declined in much of the native range in eastern North America and populations are typically relegated to small headwater streams in Connecticut, USA. We used sibship reconstruction to infer mating systems, dispersal and effective population size of resident (non-anadromous) brook trout in two headwater stream channel networks in Connecticut. Brook trout were captured via backpack electrofishing using spatially continuous sampling in the two headwaters (channel network lengths of 4.4 and 7.7 km). Eight microsatellite loci were genotyped in a total of 740 individuals (80–140 mm) subsampled in a stratified random design from all 50 m-reaches in which trout were captured. Sibship reconstruction indicated that males and females were both mostly polygamous although single pair matings were also inferred. Breeder sex ratio was inferred to be nearly 1:1. Few large-sized fullsib families (>3 individuals) were inferred and the majority of individuals were inferred to have no fullsibs among those fish genotyped (family size = 1). The median stream channel distance between pairs of individuals belonging to the same large-sized fullsib families (>3 individuals) was 100 m (range: 0–1,850 m) and 250 m (range: 0–2,350 m) in the two study sites, indicating limited dispersal at least for the size class of individuals analyzed. Using a sibship assignment method, the effective population size for the two streams was estimated at 91 (95%CI: 67–123) and 210 (95%CI: 172–259), corresponding to the ratio of effective-to-census population size of 0.06 and 0.12, respectively. Both-sex polygamy, low variation in reproductive success, and a balanced sex ratio may help maintain genetic diversity of brook trout populations with small breeder sizes persisting in headwater channel networks.

BagReg: Protein inference through machine learning.

Science.gov (United States)

Zhao, Can; Liu, Dao; Teng, Ben; He, Zengyou

2015-08-01

Protein inference from the identified peptides is of primary importance in the shotgun proteomics. The target of protein inference is to identify whether each candidate protein is truly present in the sample. To date, many computational methods have been proposed to solve this problem. However, there is still no method that can fully utilize the information hidden in the input data. In this article, we propose a learning-based method named BagReg for protein inference. The method firstly artificially extracts five features from the input data, and then chooses each feature as the class feature to separately build models to predict the presence probabilities of proteins. Finally, the weak results from five prediction models are aggregated to obtain the final result. We test our method on six public available data sets. The experimental results show that our method is superior to the state-of-the-art protein inference algorithms. Copyright © 2015 Elsevier Ltd. All rights reserved.

Ensemble stacking mitigates biases in inference of synaptic connectivity.

Science.gov (United States)

Chambers, Brendan; Levy, Maayan; Dechery, Joseph B; MacLean, Jason N

2018-01-01

A promising alternative to directly measuring the anatomical connections in a neuronal population is inferring the connections from the activity. We employ simulated spiking neuronal networks to compare and contrast commonly used inference methods that identify likely excitatory synaptic connections using statistical regularities in spike timing. We find that simple adjustments to standard algorithms improve inference accuracy: A signing procedure improves the power of unsigned mutual-information-based approaches and a correction that accounts for differences in mean and variance of background timing relationships, such as those expected to be induced by heterogeneous firing rates, increases the sensitivity of frequency-based methods. We also find that different inference methods reveal distinct subsets of the synaptic network and each method exhibits different biases in the accurate detection of reciprocity and local clustering. To correct for errors and biases specific to single inference algorithms, we combine methods into an ensemble. Ensemble predictions, generated as a linear combination of multiple inference algorithms, are more sensitive than the best individual measures alone, and are more faithful to ground-truth statistics of connectivity, mitigating biases specific to single inference methods. These weightings generalize across simulated datasets, emphasizing the potential for the broad utility of ensemble-based approaches.

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.

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.

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

Super Resolution Fluorescence Microscopy and Tracking of Bacterial Flotillin (Reggie Paralogs Provide Evidence for Defined-Sized Protein Microdomains within the Bacterial Membrane but Absence of Clusters Containing Detergent-Resistant Proteins.

Directory of Open Access Journals (Sweden)

Felix Dempwolff

2016-06-01

Full Text Available Biological membranes have been proposed to contain microdomains of a specific lipid composition, in which distinct groups of proteins are clustered. Flotillin-like proteins are conserved between pro-and eukaryotes, play an important function in several eukaryotic and bacterial cells, and define in vertebrates a type of so-called detergent-resistant microdomains. Using STED microscopy, we show that two bacterial flotillins, FloA and FloT, form defined assemblies with an average diameter of 85 to 110 nm in the model bacterium Bacillus subtilis. Interestingly, flotillin microdomains are of similar size in eukaryotic cells. The soluble domains of FloA form higher order oligomers of up to several hundred kDa in vitro, showing that like eukaryotic flotillins, bacterial assemblies are based in part on their ability to self-oligomerize. However, B. subtilis paralogs show significantly different diffusion rates, and consequently do not colocalize into a common microdomain. Dual colour time lapse experiments of flotillins together with other detergent-resistant proteins in bacteria show that proteins colocalize for no longer than a few hundred milliseconds, and do not move together. Our data reveal that the bacterial membrane contains defined-sized protein domains rather than functional microdomains dependent on flotillins. Based on their distinct dynamics, FloA and FloT confer spatially distinguishable activities, but do not serve as molecular scaffolds.

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.

State-Space Inference and Learning with Gaussian Processes

OpenAIRE

Turner, R; Deisenroth, MP; Rasmussen, CE

2010-01-01

18.10.13 KB. Ok to add author version to spiral, authors hold copyright. State-space inference and learning with Gaussian processes (GPs) is an unsolved problem. We propose a new, general methodology for inference and learning in nonlinear state-space models that are described probabilistically by non-parametric GP models. We apply the expectation maximization algorithm to iterate between inference in the latent state-space and learning the parameters of the underlying GP dynamics model. C...

Enhancing Transparency and Control When Drawing Data-Driven Inferences About Individuals.

Science.gov (United States)

Chen, Daizhuo; Fraiberger, Samuel P; Moakler, Robert; Provost, Foster

2017-09-01

Recent studies show the remarkable power of fine-grained information disclosed by users on social network sites to infer users' personal characteristics via predictive modeling. Similar fine-grained data are being used successfully in other commercial applications. In response, attention is turning increasingly to the transparency that organizations provide to users as to what inferences are drawn and why, as well as to what sort of control users can be given over inferences that are drawn about them. In this article, we focus on inferences about personal characteristics based on information disclosed by users' online actions. As a use case, we explore personal inferences that are made possible from "Likes" on Facebook. We first present a means for providing transparency into the information responsible for inferences drawn by data-driven models. We then introduce the "cloaking device"-a mechanism for users to inhibit the use of particular pieces of information in inference. Using these analytical tools we ask two main questions: (1) How much information must users cloak to significantly affect inferences about their personal traits? We find that usually users must cloak only a small portion of their actions to inhibit inference. We also find that, encouragingly, false-positive inferences are significantly easier to cloak than true-positive inferences. (2) Can firms change their modeling behavior to make cloaking more difficult? The answer is a definitive yes. We demonstrate a simple modeling change that requires users to cloak substantially more information to affect the inferences drawn. The upshot is that organizations can provide transparency and control even into complicated, predictive model-driven inferences, but they also can make control easier or harder for their users.

"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 ...

Fused Regression for Multi-source Gene Regulatory Network Inference.

Directory of Open Access Journals (Sweden)

Kari Y Lam

2016-12-01

Full Text Available Understanding gene regulatory networks is critical to understanding cellular differentiation and response to external stimuli. Methods for global network inference have been developed and applied to a variety of species. Most approaches consider the problem of network inference independently in each species, despite evidence that gene regulation can be conserved even in distantly related species. Further, network inference is often confined to single data-types (single platforms and single cell types. We introduce a method for multi-source network inference that allows simultaneous estimation of gene regulatory networks in multiple species or biological processes through the introduction of priors based on known gene relationships such as orthology incorporated using fused regression. This approach improves network inference performance even when orthology mapping and conservation are incomplete. We refine this method by presenting an algorithm that extracts the true conserved subnetwork from a larger set of potentially conserved interactions and demonstrate the utility of our method in cross species network inference. Last, we demonstrate our method's utility in learning from data collected on different experimental platforms.

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.

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

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)

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.

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…

Spatially patterned matrix elasticity directs stem cell fate

Science.gov (United States)

Yang, Chun; DelRio, Frank W.; Ma, Hao; Killaars, Anouk R.; Basta, Lena P.; Kyburz, Kyle A.; Anseth, Kristi S.

2016-08-01

There is a growing appreciation for the functional role of matrix mechanics in regulating stem cell self-renewal and differentiation processes. However, it is largely unknown how subcellular, spatial mechanical variations in the local extracellular environment mediate intracellular signal transduction and direct cell fate. Here, the effect of spatial distribution, magnitude, and organization of subcellular matrix mechanical properties on human mesenchymal stem cell (hMSCs) function was investigated. Exploiting a photodegradation reaction, a hydrogel cell culture substrate was fabricated with regions of spatially varied and distinct mechanical properties, which were subsequently mapped and quantified by atomic force microscopy (AFM). The variations in the underlying matrix mechanics were found to regulate cellular adhesion and transcriptional events. Highly spread, elongated morphologies and higher Yes-associated protein (YAP) activation were observed in hMSCs seeded on hydrogels with higher concentrations of stiff regions in a dose-dependent manner. However, when the spatial organization of the mechanically stiff regions was altered from a regular to randomized pattern, lower levels of YAP activation with smaller and more rounded cell morphologies were induced in hMSCs. We infer from these results that irregular, disorganized variations in matrix mechanics, compared with regular patterns, appear to disrupt actin organization, and lead to different cell fates; this was verified by observations of lower alkaline phosphatase (ALP) activity and higher expression of CD105, a stem cell marker, in hMSCs in random versus regular patterns of mechanical properties. Collectively, this material platform has allowed innovative experiments to elucidate a novel spatial mechanical dosing mechanism that correlates to both the magnitude and organization of spatial stiffness.

Automatic physical inference with information maximizing neural networks

Science.gov (United States)

Charnock, Tom; Lavaux, Guilhem; Wandelt, Benjamin D.

2018-04-01

Compressing large data sets to a manageable number of summaries that are informative about the underlying parameters vastly simplifies both frequentist and Bayesian inference. When only simulations are available, these summaries are typically chosen heuristically, so they may inadvertently miss important information. We introduce a simulation-based machine learning technique that trains artificial neural networks to find nonlinear functionals of data that maximize Fisher information: information maximizing neural networks (IMNNs). In test cases where the posterior can be derived exactly, likelihood-free inference based on automatically derived IMNN summaries produces nearly exact posteriors, showing that these summaries are good approximations to sufficient statistics. In a series of numerical examples of increasing complexity and astrophysical relevance we show that IMNNs are robustly capable of automatically finding optimal, nonlinear summaries of the data even in cases where linear compression fails: inferring the variance of Gaussian signal in the presence of noise, inferring cosmological parameters from mock simulations of the Lyman-α forest in quasar spectra, and inferring frequency-domain parameters from LISA-like detections of gravitational waveforms. In this final case, the IMNN summary outperforms linear data compression by avoiding the introduction of spurious likelihood maxima. We anticipate that the automatic physical inference method described in this paper will be essential to obtain both accurate and precise cosmological parameter estimates from complex and large astronomical data sets, including those from LSST and Euclid.

The spatial decision-supporting system combination of RBR & CBR based on artificial neural network and association rules

Science.gov (United States)

Tian, Yangge; Bian, Fuling

2007-06-01

The technology of artificial intelligence should be imported on the basis of the geographic information system to bring up the spatial decision-supporting system (SDSS). The paper discusses the structure of SDSS, after comparing the characteristics of RBR and CBR, the paper brings up the frame of a spatial decisional system that combines RBR and CBR, which has combined the advantages of them both. And the paper discusses the CBR in agriculture spatial decisions, the application of ANN (Artificial Neural Network) in CBR, and enriching the inference rule base based on association rules, etc. And the paper tests and verifies the design of this system with the examples of the evaluation of the crops' adaptability.

Snapshot science: new research possibilities facilitated by spatially dense data sets in limnology

Science.gov (United States)

Stanley, E. H.; Loken, L. C.; Crawford, J.; Butitta, V.; Schramm, P.

2017-12-01

The recent increase in availability of high frequency sensors is transforming the study of inland aquatic ecosystems, allowing the detection of rare or difficult-to-capture events, revealing previously unappreciated temporal dynamics, and providing rich data sets that can be used to calibrate or inform process-based models in ways that have not previously been possible. Yet sensor deployment is typically a 1-D practice, so insights are tempered by device placement. Limnologists have long known that there can be substantial spatial variability in physical, chemical, and biological features within water bodies, but in most cases, logistical difficulties limit our ability to quantify this heterogeneity. Recent improvements in remote sensing are helping to overcome this deficit for a subset of variables. Alternatively, devices such as the Fast Limnology Automated Measurement platform that deploy sensors on watercraft can be used to quickly generate spatially-rich data sets. This expanded capacity leads to new questions about what can be seen and learned about underlying processes. Surveys of multiple Wisconsin lakes reveal both homogeneity and heterogeneity among sites and variables, indicating that the limnological tradition of sampling at a single fixed point is unlikely to represent the entire lake area. Initial inferences drawn from surface water maps include identification of biogeochemical hotspots or areas of elevated loading. At a more sophisticated level, evaluation of changes in spatial structure among sites or dates is commonly used to infer process by landscape ecologists, and these same practices can now be applied to lakes and rivers. For example, a recent study documented significant changes in spatial variance and the magnitude of spatial autocorrelation of phycocyanin prior to the onset of a cyanobacterial bloom. This may provide information on population growth dynamics of cyanobacteria, and be used as early warnings of impending algal blooms. As the

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…

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.

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...

Bacterial Contamination of Boar Semen and its Relationship to Sperm Quality Preserved in Commercial Extender Containing Gentamicin Sulfate.

Science.gov (United States)

Gączarzewicz, D; Udała, J; Piasecka, M; Błaszczyk, B; Stankiewicz, T

2016-09-01

This study was designed to determine the degree and type of bacterial contamination in boar semen (79 ejaculates from Large White and Landrace boars) and its consequences for sperm quality during storage (27 extended semen samples, 16°C for five days) under practical conditions of artificial insemination (AI). The results revealed the presence of aerobic bacteria in 99% of the ejaculates (from 80 to 370 ×106 colony-forming units/mL). Most of the ejaculates contained two or three bacterial contaminants, while the Staphylococcus, Streptococcus, and Pseudomonas bacterial genera were most frequently isolated. Also detected were Enterobacter spp., Bacillus spp., Proteus spp., Escherichia coli, P. fluorescens, and P. aeruginosa. In general, the growth of certain bacterial types isolated prior to semen processing (Enterobacter spp., E. coli, P. fluorescens, and P. aeruginosa) was not discovered on different days of storage, but fluctuations (with a tendency towards increases) were found in the frequencies of Bacillus spp., Pseudomonas spp., and Staphylococcus spp. isolates up to the end of storage. Semen preserved for five days exhibited decreases in sperm motility and increases in the average number of total aerobic bacteria; this was associated with sperm agglutination, plasma membrane disruption, and acrosome damage. We inferred that, due to the different degrees and types of bacterial contaminants in the boar ejaculates, the inhibitory activity of some antimicrobial agents used in swine extenders (such as gentamicin sulfate) may be limited. Because such agents can contribute to the overgrowth of certain aerobic bacteria and a reduction in the quality of stored semen, procedures with high standards of hygiene and microbiological control should be used when processing boar semen.

Bayesian methods for hackers probabilistic programming and Bayesian inference

CERN Document Server

Davidson-Pilon, Cameron

2016-01-01

Bayesian methods of inference are deeply natural and extremely powerful. However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice–freeing you to get results using computing power. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention. Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples a...

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.

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....

The dynamic nature and territory of transcriptional machinery in the bacterial chromosome

Directory of Open Access Journals (Sweden)

Ding Jun Jin

2015-05-01

Full Text Available Our knowledge of the regulation of genes involved in bacterial growth and stress responses is extensive; however, we have only recently begun to understand how environmental cues influence the dynamic, three-dimensional distribution of RNA polymerase (RNAP in Escherichia coli on the level of single cell, using wide-field fluorescence microscopy and state-of-the-art imaging techniques. Live-cell imaging using either an agarose-embedding procedure or a microfluidic system further underscores the dynamic nature of the distribution of RNAP in response to changes in the environment. A general agreement between live-cell and fixed-cell images has validated the formaldehyde-fixing procedure, which is a technical breakthrough in the study of the cell biology of RNAP. In this review we use a systems biology perspective to summarize the advances in the cell biology of RNAP in E. coli, including the discoveries of the bacterial nucleolus, the spatial compartmentalization of the transcription machinery at the periphery of the nucleoid, and the segregation of the chromosome territories for the two major cellular functions of transcription and replication in fast-growing cells. Our understanding of the coupling of transcription and bacterial chromosome (or nucleoid structure is also summarized. Using E. coli as a simple model system, co-imaging of RNAP with DNA and other factors during growth and stress responses will continue to be a useful tool for studying bacterial growth and adaptation in changing environment.

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.

Assessment of network inference methods: how to cope with an underdetermined problem.

Directory of Open Access Journals (Sweden)

Caroline Siegenthaler

Full Text Available The inference of biological networks is an active research area in the field of systems biology. The number of network inference algorithms has grown tremendously in the last decade, underlining the importance of a fair assessment and comparison among these methods. Current assessments of the performance of an inference method typically involve the application of the algorithm to benchmark datasets and the comparison of the network predictions against the gold standard or reference networks. While the network inference problem is often deemed underdetermined, implying that the inference problem does not have a (unique solution, the consequences of such an attribute have not been rigorously taken into consideration. Here, we propose a new procedure for assessing the performance of gene regulatory network (GRN inference methods. The procedure takes into account the underdetermined nature of the inference problem, in which gene regulatory interactions that are inferable or non-inferable are determined based on causal inference. The assessment relies on a new definition of the confusion matrix, which excludes errors associated with non-inferable gene regulations. For demonstration purposes, the proposed assessment procedure is applied to the DREAM 4 In Silico Network Challenge. The results show a marked change in the ranking of participating methods when taking network inferability into account.

Spatial organization of frequency preference and selectivity in the human inferior colliculus

OpenAIRE

De Martino, Federico; Moerel, Michelle; van de Moortele, Pierre-Francois; Ugurbil, Kamil; Goebel, Rainer; Yacoub, Essa; Formisano, Elia

2013-01-01

To date, the functional organization of human auditory sub-cortical structures can only be inferred from animal models. Here we use high-resolution functional MRI at ultra-high magnetic fields (7 Tesla) to map the organization of spectral responses in the human inferior colliculus (hIC), a sub-cortical structure fundamental for sound processing. We reveal a tonotopic map with a spatial gradient of preferred frequencies approximately oriented from dorso-lateral (low frequencies) to ventro-medi...

Differentiation of bacterial and non-bacterial community-acquired pneumonia by thin-section computed tomography

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Ito, Isao [Department of Respiratory Medicine, Kurashiki Central Hospital, 1-1-1 Miwa, Kurashiki 710-8602 (Japan); Department of Respiratory Medicine, Kyoto University, 54 Shogoin-kawaharacho, Sakyo-ku, Kyoto 606-8507 (Japan)], E-mail: isaoito@kuhp.kyoto-u.ac.jp; Ishida, Tadashi [Department of Respiratory Medicine, Kurashiki Central Hospital, 1-1-1 Miwa, Kurashiki 710-8602 (Japan)], E-mail: ishidat@kchnet.or.jp; Togashi, Kaori [Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University, 54 Shogoin-kawaharacho, Sakyo-ku, Kyoto 606-8507 (Japan)], E-mail: ktogashi@kuhp.kyoto-u.ac.jp; Niimi, Akio [Department of Respiratory Medicine, Kyoto University, 54 Shogoin-kawaharacho, Sakyo-ku, Kyoto 606-8507 (Japan)], E-mail: niimi@kuhp.kyoto-u.ac.jp; Koyama, Hiroshi [General Internal Medicine, National Hospital Organization Kyoto Medical Center, 1-1 Fukakusa-Mukohatacho, Fushimi-ku, Kyoto 612-8555 (Japan)], E-mail: hkoyama-kyt@umin.ac.jp; Ishimori, Takayoshi [Department of Radiology, Kurashiki Central Hospital, 1-1-1 Miwa, Kurashiki 710-8602 (Japan)], E-mail: ti10794@kchnet.or.jp; Kobayashi, Hisataka [Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University, 54 Shogoin-kawaharacho, Sakyo-ku, Kyoto 606-8507 (Japan); Molecular Imaging Program, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 10, Room 1B40, MSC1088, 10 Center Drive, Bethesda, MD 20892-1088 (United States)], E-mail: kobayash@mail.nih.gov; Mishima, Michiaki [Department of Respiratory Medicine, Kyoto University, 54 Shogoin-kawaharacho, Sakyo-ku, Kyoto 606-8507 (Japan)], E-mail: mishima@kuhp.kyoto-u.ac.jp

2009-12-15

Background and objective: The management of community-acquired pneumonia (CAP) depends, in part, on the identification of the causative agents. The objective of this study was to determine the potential of thin-section computed tomography (CT) in differentiating bacterial and non-bacterial pneumonia. Patients and methods: Thin-section CT studies were prospectively examined in hospitalized CAP patients within 2 days of admission, followed by retrospective assessment by two pulmonary radiologists. Thin-section CT findings on the pneumonias caused by each pathogen were examined, and two types of pneumonias were compared. Using multivariate logistic regression analyses, receiver operating characteristic (ROC) curves were produced. Results: Among 183 CAP episodes (181 patients, 125 men and 56 women, mean age {+-} S.D.: 61.1 {+-} 19.7) examined by thin-section CT, the etiologies of 125 were confirmed (94 bacterial pneumonia and 31 non-bacterial pneumonia). Centrilobular nodules were specific for non-bacterial pneumonia and airspace nodules were specific for bacterial pneumonia (specificities of 89% and 94%, respectively) when located in the outer lung areas. When centrilobular nodules were the principal finding, they were specific but lacked sensitivity for non-bacterial pneumonia (specificity 98% and sensitivity 23%). To distinguish the two types of pneumonias, centrilobular nodules, airspace nodules and lobular shadows were found to be important by multivariate analyses. ROC curve analysis discriminated bacterial pneumonia from non-bacterial pneumonia among patients without underlying lung diseases, yielding an optimal point with sensitivity and specificity of 86% and 79%, respectively, but was less effective when all patients were analyzed together (70% and 84%, respectively). Conclusion: Thin-section CT examination was applied for the differentiation of bacterial and non-bacterial pneumonias. Though showing some potential, this examination at the present time would

Differentiation of bacterial and non-bacterial community-acquired pneumonia by thin-section computed tomography

International Nuclear Information System (INIS)

Ito, Isao; Ishida, Tadashi; Togashi, Kaori; Niimi, Akio; Koyama, Hiroshi; Ishimori, Takayoshi; Kobayashi, Hisataka; Mishima, Michiaki

2009-01-01

Background and objective: The management of community-acquired pneumonia (CAP) depends, in part, on the identification of the causative agents. The objective of this study was to determine the potential of thin-section computed tomography (CT) in differentiating bacterial and non-bacterial pneumonia. Patients and methods: Thin-section CT studies were prospectively examined in hospitalized CAP patients within 2 days of admission, followed by retrospective assessment by two pulmonary radiologists. Thin-section CT findings on the pneumonias caused by each pathogen were examined, and two types of pneumonias were compared. Using multivariate logistic regression analyses, receiver operating characteristic (ROC) curves were produced. Results: Among 183 CAP episodes (181 patients, 125 men and 56 women, mean age ± S.D.: 61.1 ± 19.7) examined by thin-section CT, the etiologies of 125 were confirmed (94 bacterial pneumonia and 31 non-bacterial pneumonia). Centrilobular nodules were specific for non-bacterial pneumonia and airspace nodules were specific for bacterial pneumonia (specificities of 89% and 94%, respectively) when located in the outer lung areas. When centrilobular nodules were the principal finding, they were specific but lacked sensitivity for non-bacterial pneumonia (specificity 98% and sensitivity 23%). To distinguish the two types of pneumonias, centrilobular nodules, airspace nodules and lobular shadows were found to be important by multivariate analyses. ROC curve analysis discriminated bacterial pneumonia from non-bacterial pneumonia among patients without underlying lung diseases, yielding an optimal point with sensitivity and specificity of 86% and 79%, respectively, but was less effective when all patients were analyzed together (70% and 84%, respectively). Conclusion: Thin-section CT examination was applied for the differentiation of bacterial and non-bacterial pneumonias. Though showing some potential, this examination at the present time would not

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.

Spatially uniform but temporally variable bacterioplankton in a semi-enclosed coastal area.

Science.gov (United States)

Meziti, Alexandra; Kormas, Konstantinos A; Moustaka-Gouni, Maria; Karayanni, Hera

2015-07-01

Studies focusing on the temporal and spatial dynamics of bacterioplankton communities within littoral areas undergoing direct influences from the coast are quite limited. In addition, they are more complicated to resolve compared to communities in the open ocean. In order to elucidate the effects of spatial vs. temporal variability on bacterial communities in a highly land-influenced semi-enclosed gulf, surface bacterioplankton communities from five coastal sites in Igoumenitsa Gulf (Ionian Sea, Greece) were analyzed over a nine-month period using 16S rDNA 454-pyrosequencing. Temporal differences were more pronounced than spatial ones, with lower diversity indices observed during the summer months. During winter and early spring, bacterial communities were dominated by SAR11 representatives, while this pattern changed in May when they were abruptly replaced by members of Flavobacteriales, Pseudomonadales, and Alteromonadales. Additionally, correlation analysis showed high negative correlations between the presence of SAR11 OTUs in relation to temperature and sunlight that might have driven, directly or indirectly, the disappearance of these OTUs in the summer months. The dominance of SAR11 during the winter months further supported the global distribution of the clade, not only in the open-sea, but also in coastal systems. This study revealed that specific bacteria exhibited distinct succession patterns in an anthropogenic-impacted coastal system. The major bacterioplankton component was represented by commonly found marine bacteria exhibiting seasonal dynamics, while freshwater and terrestrial-related phylotypes were absent. Copyright © 2015 Elsevier GmbH. All rights reserved.

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.

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

An algebra-based method for inferring gene regulatory networks.

Science.gov (United States)

Vera-Licona, Paola; Jarrah, Abdul; Garcia-Puente, Luis David; McGee, John; Laubenbacher, Reinhard

2014-03-26

The inference of gene regulatory networks (GRNs) from experimental observations is at the heart of systems biology. This includes the inference of both the network topology and its dynamics. While there are many algorithms available to infer the network topology from experimental data, less emphasis has been placed on methods that infer network dynamics. Furthermore, since the network inference problem is typically underdetermined, it is essential to have the option of incorporating into the inference process, prior knowledge about the network, along with an effective description of the search space of dynamic models. Finally, it is also important to have an understanding of how a given inference method is affected by experimental and other noise in the data used. This paper contains a novel inference algorithm using the algebraic framework of Boolean polynomial dynamical systems (BPDS), meeting all these requirements. The algorithm takes as input time series data, including those from network perturbations, such as knock-out mutant strains and RNAi experiments. It allows for the incorporation of prior biological knowledge while being robust to significant levels of noise in the data used for inference. It uses an evolutionary algorithm for local optimization with an encoding of the mathematical models as BPDS. The BPDS framework allows an effective representation of the search space for algebraic dynamic models that improves computational performance. The algorithm is validated with both simulated and experimental microarray expression profile data. Robustness to noise is tested using a published mathematical model of the segment polarity gene network in Drosophila melanogaster. Benchmarking of the algorithm is done by comparison with a spectrum of state-of-the-art network inference methods on data from the synthetic IRMA network to demonstrate that our method has good precision and recall for the network reconstruction task, while also predicting several of the

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.

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...

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

Bayesian Inference and Online Learning in Poisson Neuronal Networks.

Science.gov (United States)

Huang, Yanping; Rao, Rajesh P N

2016-08-01

Motivated by the growing evidence for Bayesian computation in the brain, we show how a two-layer recurrent network of Poisson neurons can perform both approximate Bayesian inference and learning for any hidden Markov model. The lower-layer sensory neurons receive noisy measurements of hidden world states. The higher-layer neurons infer a posterior distribution over world states via Bayesian inference from inputs generated by sensory neurons. We demonstrate how such a neuronal network with synaptic plasticity can implement a form of Bayesian inference similar to Monte Carlo methods such as particle filtering. Each spike in a higher-layer neuron represents a sample of a particular hidden world state. The spiking activity across the neural population approximates the posterior distribution over hidden states. In this model, variability in spiking is regarded not as a nuisance but as an integral feature that provides the variability necessary for sampling during inference. We demonstrate how the network can learn the likelihood model, as well as the transition probabilities underlying the dynamics, using a Hebbian learning rule. We present results illustrating the ability of the network to perform inference and learning for arbitrary hidden Markov models.

Contingency inferences driven by base rates: Valid by sampling

Directory of Open Access Journals (Sweden)

Florian Kutzner

2011-04-01

Full Text Available Fiedler et al. (2009, reviewed evidence for the utilization of a contingency inference strategy termed pseudocontingencies (PCs. In PCs, the more frequent levels (and, by implication, the less frequent levels are assumed to be associated. PCs have been obtained using a wide range of task settings and dependent measures. Yet, the readiness with which decision makers rely on PCs is poorly understood. A computer simulation explored two potential sources of subjective validity of PCs. First, PCs are shown to perform above chance level when the task is to infer the sign of moderate to strong population contingencies from a sample of observations. Second, contingency inferences based on PCs and inferences based on cell frequencies are shown to partially agree across samples. Intriguingly, this criterion and convergent validity are by-products of random sampling error, highlighting the inductive nature of contingency inferences.

Accessibility versus accuracy in retrieving spatial memory: evidence for suboptimal assumed headings.

Science.gov (United States)

Yerramsetti, Ashok; Marchette, Steven A; Shelton, Amy L

2013-07-01

Orientation dependence in spatial memory has often been interpreted in terms of accessibility: Object locations are encoded relative to a reference orientation that affords the most accurate access to spatial memory. An open question, however, is whether people naturally use this "preferred" orientation whenever recalling the space. We tested this question by asking participants to locate buildings on a familiar campus from various imagined locations, without specifying the heading to be assumed. We then used these pointing judgments to infer the approximate heading participants assumed at each location. Surprisingly, each location showed a unique assumed heading that was consistent across participants and seemed to reflect episodic or visual properties of the space. This result suggests that although locations are encoded relative to a reference orientation, other factors may influence how people choose to access the stored information and whether they appeal to long-term spatial memory or other more sensory-based stores. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Reinforcement and inference in cross-situational word learning.

Science.gov (United States)

Tilles, Paulo F C; Fontanari, José F

2013-01-01

Cross-situational word learning is based on the notion that a learner can determine the referent of a word by finding something in common across many observed uses of that word. Here we propose an adaptive learning algorithm that contains a parameter that controls the strength of the reinforcement applied to associations between concurrent words and referents, and a parameter that regulates inference, which includes built-in biases, such as mutual exclusivity, and information of past learning events. By adjusting these parameters so that the model predictions agree with data from representative experiments on cross-situational word learning, we were able to explain the learning strategies adopted by the participants of those experiments in terms of a trade-off between reinforcement and inference. These strategies can vary wildly depending on the conditions of the experiments. For instance, for fast mapping experiments (i.e., the correct referent could, in principle, be inferred in a single observation) inference is prevalent, whereas for segregated contextual diversity experiments (i.e., the referents are separated in groups and are exhibited with members of their groups only) reinforcement is predominant. Other experiments are explained with more balanced doses of reinforcement and inference.