Long-term Impact of sewage sludge application on rhizobium leguminosarum biovar trifolii: an evaluation using meta-analysis

The Long-Term Sludge Experiment (LTSE) began in 1994 at nine UK field sites as part of continuing research into the effects of sludge-borne heavy metals on soil fertility. The long-term effects of Zn, Cu, and Cd on the most probable numbers of cells (MPN) of Rhizobium leguminosarum biovar trifolii were monitored for 8 yr in sludge-amended soils. To assess the statutory limits set by the UK Sludge (Use in Agriculture) Regulations, the experimental data were reviewed using statistical methods of meta-analysis. Previous LTSE studies have focused predominantly on statistical significance rather than effect size, whereas meta-analysis focuses on the magnitude and direction of an effect, i.e., the practical significance rather than its statistical significance. Results showed Zn to be the most toxic element causing an overall significant decrease in Rhizobium MPN of −26.6% during the LTSE. The effect of Cu showed no significant effect on Rhizobium MPN at concentrations below the UK limits, although a −5% decrease in Rhizobium MPN was observed in soils where total Cu ranged from 100 to <135 mg kg−1. Overall, there was nothing to indicate that Cd had a significant effect on Rhizobium MPN below the current UK statutory limit. In summary, the UK statutory limit for Zn appears to be insufficient for protecting Rhizobium from Zn toxicity effects

Comments on "Particle Markov chain Monte Carlo" by C. Andrieu, A. Doucet, and R. Hollenstein

This is the compilation of our comments submitted to the Journal of the Royal Statistical Society, Series B, to be published within the discussion of the Read Paper of Andrieu, Doucet and Hollenstein.Comment: 7 pages, 4 figure

Efficient Sequential Monte-Carlo Samplers for Bayesian Inference

In many problems, complex non-Gaussian and/or nonlinear models are required to accurately describe a physical system of interest. In such cases, Monte Carlo algorithms are remarkably flexible and extremely powerful approaches to solve such inference problems. However, in the presence of a high-dimensional and/or multimodal posterior distribution, it is widely documented that standard Monte-Carlo techniques could lead to poor performance. In this paper, the study is focused on a Sequential Monte-Carlo (SMC) sampler framework, a more robust and efficient Monte Carlo algorithm. Although this approach presents many advantages over traditional Monte-Carlo methods, the potential of this emergent technique is however largely underexploited in signal processing. In this work, we aim at proposing some novel strategies that will improve the efficiency and facilitate practical implementation of the SMC sampler specifically for signal processing applications. Firstly, we propose an automatic and adaptive strategy that selects the sequence of distributions within the SMC sampler that minimizes the asymptotic variance of the estimator of the posterior normalization constant. This is critical for performing model selection in modelling applications in Bayesian signal processing. The second original contribution we present improves the global efficiency of the SMC sampler by introducing a novel correction mechanism that allows the use of the particles generated through all the iterations of the algorithm (instead of only particles from the last iteration). This is a significant contribution as it removes the need to discard a large portion of the samples obtained, as is standard in standard SMC methods. This will improve estimation performance in practical settings where computational budget is important to consider.Comment: arXiv admin note: text overlap with arXiv:1303.3123 by other author

Nonasymptotic analysis of adaptive and annealed Feynman-Kac particle models

Sequential and quantum Monte Carlo methods, as well as genetic type search algorithms can be interpreted as a mean field and interacting particle approximations of Feynman-Kac models in distribution spaces. The performance of these population Monte Carlo algorithms is strongly related to the stability properties of nonlinear Feynman-Kac semigroups. In this paper, we analyze these models in terms of Dobrushin ergodic coefficients of the reference Markov transitions and the oscillations of the potential functions. Sufficient conditions for uniform concentration inequalities w.r.t. time are expressed explicitly in terms of these two quantities. We provide an original perturbation analysis that applies to annealed and adaptive Feynman-Kac models, yielding what seems to be the first results of this kind for these types of models. Special attention is devoted to the particular case of Boltzmann-Gibbs measures' sampling. In this context, we design an explicit way of tuning the number of Markov chain Monte Carlo iterations with temperature schedule. We also design an alternative interacting particle method based on an adaptive strategy to define the temperature increments. The theoretical analysis of the performance of this adaptive model is much more involved as both the potential functions and the reference Markov transitions now depend on the random evolution on the particle model. The nonasymptotic analysis of these complex adaptive models is an open research problem. We initiate this study with the concentration analysis of a simplified adaptive models based on reference Markov transitions that coincide with the limiting quantities, as the number of particles tends to infinity.Comment: Published at http://dx.doi.org/10.3150/14-BEJ680 in the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm

Sequential Monte Carlo EM for multivariate probit models

Multivariate probit models (MPM) have the appealing feature of capturing some of the dependence structure between the components of multidimensional binary responses. The key for the dependence modelling is the covariance matrix of an underlying latent multivariate Gaussian. Most approaches to MLE in multivariate probit regression rely on MCEM algorithms to avoid computationally intensive evaluations of multivariate normal orthant probabilities. As an alternative to the much used Gibbs sampler a new SMC sampler for truncated multivariate normals is proposed. The algorithm proceeds in two stages where samples are first drawn from truncated multivariate Student $t$ distributions and then further evolved towards a Gaussian. The sampler is then embedded in a MCEM algorithm. The sequential nature of SMC methods can be exploited to design a fully sequential version of the EM, where the samples are simply updated from one iteration to the next rather than resampled from scratch. Recycling the samples in this manner significantly reduces the computational cost. An alternative view of the standard conditional maximisation step provides the basis for an iterative procedure to fully perform the maximisation needed in the EM algorithm. The identifiability of MPM is also thoroughly discussed. In particular, the likelihood invariance can be embedded in the EM algorithm to ensure that constrained and unconstrained maximisation are equivalent. A simple iterative procedure is then derived for either maximisation which takes effectively no computational time. The method is validated by applying it to the widely analysed Six Cities dataset and on a higher dimensional simulated example. Previous approaches to the Six Cities overly restrict the parameter space but, by considering the correct invariance, the maximum likelihood is quite naturally improved when treating the full unrestricted model.Comment: 26 pages, 2 figures. In press, Computational Statistics & Data Analysi

Long-term impact of sewage sludge application on soil microbial biomass: An evaluation using meta-analysis

The Long-Term Sludge Experiments (LTSE) began in 1994 as part of continuing research into the effects of sludge-borne heavy metals on soil fertility. The long-term effects of Zn, Cu, and Cd on soil microbial biomass carbon (Cmic) were monitored for 8 years (1997-2005) in sludge amended soils at nine UK field sites. To assess the statutory limits set by the UK Sludge (Use in Agriculture) Regulations the experimental data has been reviewed using the statistical methods of meta-analysis. Previous LTSE studies have focused predominantly on statistical significance rather than effect size, whereas meta-analysis focuses on the magnitude and direction of an effect, i.e. the practical significance, rather than its statistical significance. The results presented here show that significant decreases in Cmic have occurred in soils where the total concentrations of Zn and Cu fall below the current UK statutory limits. For soils receiving sewage sludge predominantly contaminated with Zn, decreases of approximately 7–11% were observed at concentrations below the UK statutory limit. The effect of Zn appeared to increase over time, with increasingly greater decreases in Cmic observed over a period of 8 years. This may be due to an interactive effect between Zn and confounding Cu contamination which has augmented the bioavailability of these metals over time. Similar decreases (7–12%) in Cmic were observed in soils receiving sewage sludge predominantly contaminated with Cu; however, Cmic appeared to show of recovery after a period of 6 years. Application of sewage sludge predominantly contaminated with Cd appeared to have no effect on Cmic at concentrations below the current UK statutory limit

Lanthanides extraction processes in molten fluoride media. Application to nuclear spent fuel reprocessing

This paper describes four techniques of extraction of lanthanides elements (Ln) from molten salts in the general frame of reprocessing nuclear wastes; One of them is chemical: the precipitation of Ln ions in insoluble compounds (oxides or oxifluorides); the others use electrochemical methodology in molten fluorides for extraction and measurement of the progress of the processes: first electrodeposition of pure Ln metals on an inert cathode material was proved to be incomplete and cause problems for recovering the metal; electrodeposition of Ln in the form of alloys seems to be far more promising because on one hand the low activity of Ln shifts the electrodeposition potential in a more anodic range avoiding any overlapping with the solvent reduction and furthermore exhibit rapid process kinetics; two ways were examined: (i) obtention of alloys by reaction of the electroreducing Ln and the cathode in Ni or preferably in Cu, because in this case we obtain easily liquid compounds, that enhances sensibly the process kinetics; (ii) codeposition of Ln ions with aluminium ions on an inert cathode giving a well defined composition of the alloy. Each way was proved to give extraction efficiency close to unity in a moderate time

On computational tools for Bayesian data analysis

While Robert and Rousseau (2010) addressed the foundational aspects of Bayesian analysis, the current chapter details its practical aspects through a review of the computational methods available for approximating Bayesian procedures. Recent innovations like Monte Carlo Markov chain, sequential Monte Carlo methods and more recently Approximate Bayesian Computation techniques have considerably increased the potential for Bayesian applications and they have also opened new avenues for Bayesian inference, first and foremost Bayesian model choice.Comment: This is a chapter for the book "Bayesian Methods and Expert Elicitation" edited by Klaus Bocker, 23 pages, 9 figure

Experimenting with ecosystem interaction networks in search of threshold potentials in real-world marine ecosystems

Thresholds profoundly affect our understanding and management of ecosystem dynamics, but we have yet to develop practical techniques to assess the risk that thresholds will be crossed. Combining ecological knowledge of critical system interdependencies with a large-scale experiment, we tested for breaks in the ecosystem interaction network to identify threshold potential in real-world ecosystem dynamics. Our experiment with the bivalves Macomona liliana and Austrovenus stutchburyi on marine sandflats in New Zealand demonstrated that reductions in incident sunlight changed the interaction network between sediment biogeochemical fluxes, productivity, and macrofauna. By demonstrating loss of positive feedbacks and changes in the architecture of the network, we provide mechanistic evidence that stressors lead to break points in dynamics, which theory predicts predispose a system to a critical transition