Monte Carlo simulation of a clearance box monitor used for nuclear power plant decommissioning.

When decommissioning a nuclear facility it is important to be able to estimate activity levels of potentially radioactive samples and compare with clearance values defined by regulatory authorities. This paper presents a method of calibrating a clearance box monitor based on practical experimental measurements and Monte Carlo simulations. Adjusting the simulation for experimental data obtained using a simple point source permits the computation of absolute calibration factors for more complex geometries with an accuracy of a bit more than 20%. The uncertainty of the calibration factor can be improved to about 10% when the simulation is used relatively, in direct comparison with a measurement performed in the same geometry but with another nuclide. The simulation can also be used to validate the experimental calibration procedure when the sample is supposed to be homogeneous but the calibration factor is derived from a plate phantom. For more realistic geometries, like a small gravel dumpster, Monte Carlo simulation shows that the calibration factor obtained with a larger homogeneous phantom is correct within about 20%, if sample density is taken as the influencing parameter. Finally, simulation can be used to estimate the effect of a contamination hotspot. The research supporting this paper shows that activity could be largely underestimated in the event of a centrally-located hotspot and overestimated for a peripherally-located hotspot if the sample is assumed to be homogeneously contaminated. This demonstrates the usefulness of being able to complement experimental methods with Monte Carlo simulations in order to estimate calibration factors that cannot be directly measured because of a lack of available material or specific geometries.

Intercomparison of the response of different photon and neutron detectors around a spent fuel cask

An intercomparison of the response of different photon and neutron detectors was performed in several measurement positions around a spent fuel cask (type TN 12/2B) filled with 4 MOX and 8 UO2 15 x 15 PWR fuel assemblies at the nuclear power plant Gosgen (KKG) in Switzerland. The instruments used in the study were both active and passive, photon and neutron detectors calibrated either for ambient or personal dose equivalent. The aim of the measurement campaign was to compare the responses of the radiation instruments to routinely used detectors. It has been shown that especially the indications of the neutron detectors are strongly dependent on the neutron spectra around the cask due to their different energy responses. However, routinely used active photon and neutron detectors were shown to be reliable instruments. (C) 2012 Elsevier Ltd. All rights reserved.

Advanced geostatistical and machine-learning models for spatial data analysis of radioactively contaminated regions

Radioactive soil-contamination mapping and risk assessment is a vital issue for decision makers. Traditional approaches for mapping the spatial concentration of radionuclides employ various regression-based models, which usually provide a single-value prediction realization accompanied (in some cases) by estimation error. Such approaches do not provide the capability for rigorous uncertainty quantification or probabilistic mapping. Machine learning is a recent and fast-developing approach based on learning patterns and information from data. Artificial neural networks for prediction mapping have been especially powerful in combination with spatial statistics. A data-driven approach provides the opportunity to integrate additional relevant information about spatial phenomena into a prediction model for more accurate spatial estimates and associated uncertainty. Machine-learning algorithms can also be used for a wider spectrum of problems than before: classification, probability density estimation, and so forth. Stochastic simulations are used to model spatial variability and uncertainty. Unlike regression models, they provide multiple realizations of a particular spatial pattern that allow uncertainty and risk quantification. This paper reviews the most recent methods of spatial data analysis, prediction, and risk mapping, based on machine learning and stochastic simulations in comparison with more traditional regression models. The radioactive fallout from the Chernobyl Nuclear Power Plant accident is used to illustrate the application of the models for prediction and classification problems. This fallout is a unique case study that provides the challenging task of analyzing huge amounts of data ('hard' direct measurements, as well as supplementary information and expert estimates) and solving particular decision-oriented problems.

Ecosystem impacts of Alpine water intakes for hydropower: the challenge of sediment management

The natural flow hydrological characteristics (such as the magnitude, frequency, duration, timing, and rate of change of discharge) of Alpine streams, dominated by snowmelt and glacier melt, have been established for many years. More recently, the ecosystems that they sustain have been described and explained. However, natural Alpine flow regimes may be strongly modified by hydroelectric power production, which impacts upon both river discharge and sediment transfer, and hence on downstream flora and fauna. The impacts of barrages or dams have been well studied. However, there is a second type of flow regulation, associated with flow abstraction at intakes where the water is transferred laterally, either to another valley for storage, or at altitude within the same valley for eventual release downstream. Like barrages, such intakes also trap sediment, but because they are much smaller, they fill more frequently and so need to be flushed regularly. Downstream, while the flow regime is substantially modified, the delivery of sediment (notably coarser fractions) remains. The ecosystem impacts of such systems have been rarely considered. Through reviewing the state of our knowledge of Alpine ecosystems, we outline the key research questions that will need to be addressed in order to modify intake management so as to reduce downstream ecological impacts. Simply redesigning river flows to address sediment management will be ineffective because such redesign cannot restore a natural sediment regime and other approaches are likely to be required if stream ecology in such systems is to be improved.

Small, stable shuttle vectors based on the minimal pVS1 replicon for use in gram-negative, plant-associated bacteria.

The minimal replicon of the Pseudomonas plasmid pVS1 was genetically defined and combined with the Escherichia coli p15A replicon, to provide a series of new, oligocopy cloning vectors (5.3 to 8.3 kb). Recombinant plasmids derived from these vectors were stable in growing and nongrowing cells of root-colonizing P. fluorescens strains incubated under different environmental conditions for more than 1 month.

Overcoming limitations of modelling rare species by using ensembles of small models

1. Species distribution models (SDMs) have become a standard tool in ecology and applied conservation biology. Modelling rare and threatened species is particularly important for conservation purposes. However, modelling rare species is difficult because the combination of few occurrences and many predictor variables easily leads to model overfitting. A new strategy using ensembles of small models was recently developed in an attempt to overcome this limitation of rare species modelling and has been tested successfully for only a single species so far. Here, we aim to test the approach more comprehensively on a large number of species including a transferability assessment. 2. For each species numerous small (here bivariate) models were calibrated, evaluated and averaged to an ensemble weighted by AUC scores. These 'ensembles of small models' (ESMs) were compared to standard Species Distribution Models (SDMs) using three commonly used modelling techniques (GLM, GBM, Maxent) and their ensemble prediction. We tested 107 rare and under-sampled plant species of conservation concern in Switzerland. 3. We show that ESMs performed significantly better than standard SDMs. The rarer the species, the more pronounced the effects were. ESMs were also superior to standard SDMs and their ensemble when they were independently evaluated using a transferability assessment. 4. By averaging simple small models to an ensemble, ESMs avoid overfitting without losing explanatory power through reducing the number of predictor variables. They further improve the reliability of species distribution models, especially for rare species, and thus help to overcome limitations of modelling rare species.

Species distribution models reveal apparent competitive and facilitative effects of a dominant species on the distribution of tundra plants

Abiotic factors are considered strong drivers of species distribution and assemblages. Yet these spatial patterns are also influenced by biotic interactions. Accounting for competitors or facilitators may improve both the fit and the predictive power of species distribution models (SDMs). We investigated the influence of a dominant species, Empetrum nigrum ssp. hermaphroditum, on the distribution of 34 subordinate species in the tundra of northern Norway. We related SDM parameters of those subordinate species to their functional traits and their co-occurrence patterns with E. hermaphroditum across three spatial scales. By combining both approaches, we sought to understand whether these species may be limited by competitive interactions and/or benefit from habitat conditions created by the dominant species. The model fit and predictive power increased for most species when the frequency of occurrence of E. hermaphroditum was included in the SDMs as a predictor. The largest increase was found for species that 1) co-occur most of the time with E. hermaphroditum, both at large (i.e. 750 m) and small spatial scale (i.e. 2 m) or co-occur with E. hermaphroditum at large scale but not at small scale and 2) have particularly low or high leaf dry matter content (LDMC). Species that do not co-occur with E. hermaphroditum at the smallest scale are generally palatable herbaceous species with low LDMC, thus showing a weak ability to tolerate resource depletion that is directly or indirectly induced by E. hermaphroditum. Species with high LDMC, showing a better aptitude to face resource depletion and grazing, are often found in the proximity of E. hermaphroditum. Our results are consistent with previous findings that both competition and facilitation structure plant distribution and assemblages in the Arctic tundra. The functional and co-occurrence approaches used were complementary and provided a deeper understanding of the observed patterns by refinement of the pool of potential direct and indirect ecological effects of E. hermaphroditum on the distribution of subordinate species. Our correlative study would benefit being complemented by experimental approaches.

Small ubiquitin-like modifier conjugating enzyme with active site mutation acts as dominant negative inhibitor of SUMO conjugation in arabidopsis(F).

Small ubiquitin-like modifier (SUMO) conjugation affects a broad range of processes in plants, including growth, flower initiation, pathogen defense, and responses to abiotic stress. Here, we investigate in vivo and in vitro a SUMO conjugating enzyme with a Cys to Ser change in the active site, and show that it has a dominant negative effect. In planta expression significantly perturbs normal development, leading to growth retardation, early flowering and gene expression changes. We suggest that the mutant protein can serve as a probe to investigate sumoylation, also in plants for which poor genetic infrastructure precludes analysis via loss-of-function mutants.

Peroxisomal polyhydroxyalkanoate biosynthesis is a promising strategy for bioplastic production in high biomass crops.

Polyhydroxyalkanoates (PHAs) are bacterial carbon storage polymers with diverse plastic-like properties. PHA biosynthesis in transgenic plants is being developed as a way to reduce the cost and increase the sustainability of industrial PHA production. The homopolymer polyhydroxybutyrate (PHB) is the simplest form of these biodegradable polyesters. Plant peroxisomes contain the substrate molecules and necessary reducing power for PHB biosynthesis, but peroxisomal PHB production has not been explored in whole soil-grown transgenic plants to date. We generated transgenic sugarcane (Saccharum sp.) with the three-enzyme Ralstonia eutropha PHA biosynthetic pathway targeted to peroxisomes. We also introduced the pathway into Arabidopsis thaliana, as a model system for studying and manipulating peroxisomal PHB production. PHB, at levels up to 1.6%-1.8% dry weight, accumulated in sugarcane leaves and A. thaliana seedlings, respectively. In sugarcane, PHB accumulated throughout most leaf cell types in both peroxisomes and vacuoles. A small percentage of total polymer was also identified as the copolymer poly (3-hydroxybutyrate-co-3-hydroxyvalerate) in both plant species. No obvious deleterious effect was observed on plant growth because of peroxisomal PHA biosynthesis at these levels. This study highlights how using peroxisomal metabolism for PHA biosynthesis could significantly contribute to reaching commercial production levels of PHAs in crop plants.

Jasmonates and related oxylipins in plant responses to pathogenesis and herbivory.

The vigorous production of oxygenated fatty acids (oxylipins) is a characteristic response to pathogenesis and herbivory, and is often accompanied by the substantial release of small and reactive lipid-fragmentation products. Some oxylipins, most notably those of the jasmonate family, have key roles as potent regulators. Recent advances have been made in understanding oxylipin-regulated signal transduction in response to attack. Much jasmonate signaling takes place via a genetically defined signal network that is linked to the ethylene, auxin, and salicylic acid signal pathways, but a second aspect of jasmonate signaling is emerging. Some jasmonates and several newly discovered cyclopentenone lipids can activate or repress gene expression through the activities of a conserved electrophilic atom group.

Enhanced production of indole-3-acetic acid by a genetically modified strain of Pseudomonas fluorescens CHA0 affects root growth of cucumber but does not improve protection of the plant against Pythium root rot

The biocontrol strain CHA0 of Pseudomonas fluorescens produces small amounts of indole-3-acetic acid via the tryptophan side chain oxidase and the tryptophan transaminase pathways. A recombinant plasmid (pME3468) expressing the tryptophan monooxygenase pathway was introduced into strain CHA0; this resulted in elevated synthesis of indole-3-acetic acid in vitro, especially after addition of -tryptophan. In natural soil, strain CHA0/pME3468 increased fresh root weight of cucumber by 17-36%, compared to the effect of strain CHA0; root colonization was about 106 cells per g of root. However, both strains gave similar protection of cucumber against Pythium ultimum. In autoclaved soil, at 6×107 cells per g of root, strain CHA0 stimulated growth of roots and shoots, whereas strain CHA0/pME3468 caused root stunting and strong reduction of plant weight. These results are in agreement with the known effects of exogenous indole-3-acetic acid on plant roots and suggest that in the system examined, indole-3-acetic acid does not contribute to the biocontrol properties of strain CHA0.

DNA microarrays and plant defence

Recent progress in understanding plant defence has highlighted a complex, interacting network of signalling pathways leading to the induction of numerous genes. The advent of new technologies for the global analysis of gene expression is fundamentally affecting research in biology, and studies on plant defence should benefit from these new approaches. Genome-wide microarrays will provide a powerful tool for the discovery of all defence-related genes and should help in elucidating their function. The association of a particular signalling pathway with a defence response can be tested with microarrays and defined mutants. Comparison of transcript profiles after biotic and abiotic stresses reveals overlapping activation of defence-related genes and defines new concepts on how plants cope with multiple aggressions. The combination of expression data with other biochemical or metabolite measurements seems another promising approach. Finally, small-scale, dedicated microarrays containing sets of well-characterised genes might prove to be a very useful complement to more expensive, less accessible, large-scale arrays.

Land use improves spatial predictions of mountain plant abundance but not presence-absence

Question Does a land-use variable improve spatial predictions of plant species presence-absence and abundance models at the regional scale in a mountain landscape? Location Western Swiss Alps. Methods Presence-absence generalized linear models (GLM) and abundance ordinal logistic regression models (LRM) were fitted to data on 78 mountain plant species, with topo-climatic and/or land-use variables available at a 25-m resolution. The additional contribution of land use when added to topo-climatic models was evaluated by: (1) assessing the changes in model fit and (2) predictive power, (3) partitioning the deviance respectively explained by the topo-climatic variables and the land-use variable through variation partitioning, and (5) comparing spatial projections. Results Land use significantly improved the fit of presence-absence models but not their predictive power. In contrast, land use significantly improved both the fit and predictive power of abundance models. Variation partitioning also showed that the individual contribution of land use to the deviance explained by presence-absence models was, on average, weak for both GLM and LRM (3.7% and 4.5%, respectively), but changes in spatial projections could nevertheless be important for some species. Conclusions In this mountain area and at our regional scale, land use is important for predicting abundance, but not presence-absence. The importance of adding land-use information depends on the species considered. Even without a marked effect on model fit and predictive performance, adding land use can affect spatial projections of both presence-absence and abundance models.

Overcoming the rare species modelling paradox: test of a novel hierarchical framework with an Iberian endemic plant

Rare species have restricted geographic ranges, habitat specialization, and/or small population sizes. Datasets on rare species distribution usually have few observations, limited spatial accuracy and lack of valid absences; conversely they provide comprehensive views of species distributions allowing to realistically capture most of their realized environmental niche. Rare species are the most in need of predictive distribution modelling but also the most difficult to model. We refer to this contrast as the "rare species modelling paradox" and propose as a solution developing modelling approaches that deal with a sufficiently large set of predictors, ensuring that statistical models aren't overfitted. Our novel approach fulfils this condition by fitting a large number of bivariate models and averaging them with a weighted ensemble approach. We further propose that this ensemble forecasting is conducted within a hierarchic multi-scale framework. We present two ensemble models for a test species, one at regional and one at local scale, each based on the combination of 630 models. In both cases, we obtained excellent spatial projections, unusual when modelling rare species. Model results highlight, from a statistically sound approach, the effects of multiple drivers in a same modelling framework and at two distinct scales. From this added information, regional models can support accurate forecasts of range dynamics under climate change scenarios, whereas local models allow the assessment of isolated or synergistic impacts of changes in multiple predictors. This novel framework provides a baseline for adaptive conservation, management and monitoring of rare species at distinct spatial and temporal scales.

Horizontal, but not vertical, biotic interactions affect fine-scale plant distribution patterns in a low-energy system

Studies of species range determinants have traditionally focused on abiotic variables (typically climatic conditions), and therefore the recent explicit consideration of biotic interactions represents an important advance in the field. While these studies clearly support the role of biotic interactions in shaping species distributions, most examine only the influence of a single species and/or a single interaction, failing to account for species being subject to multiple concurrent interactions. By fitting species distribution models (SDMs), we examine the influence of multiple vertical (i.e., grazing, trampling, and manuring by mammalian herbivores) and horizontal (i.e., competition and facilitation; estimated from the cover of dominant plant species) interspecific interactions on the occurrence and cover of 41 alpine tundra plant species. Adding plant-plant interactions to baseline SDMs (using five field-quantified abiotic variables) significantly improved models' predictive power for independent data, while herbivore-related variables had only a weak influence. Overall, abiotic variables had the strongest individual contributions to the distribution of alpine tundra plants, with the importance of horizontal interaction variables exceeding that of vertical interaction variables. These results were consistent across three modeling techniques, for both species occurrence and cover, demonstrating the pattern to be robust. Thus, the explicit consideration of multiple biotic interactions reveals that plant-plant interactions exert control over the fine-scale distribution of vascular species that is comparable to abiotic drivers and considerably stronger than herbivores in this low-energy system.