BME-based uncertainty assessment of the Chernobyl fallout

The vast territories that have been radioactively contaminated during the 1986 Chernobyl accident provide a substantial data set of radioactive monitoring data, which can be used for the verification and testing of the different spatial estimation (prediction) methods involved in risk assessment studies. Using the Chernobyl data set for such a purpose is motivated by its heterogeneous spatial structure (the data are characterized by large-scale correlations, short-scale variability, spotty features, etc.). The present work is concerned with the application of the Bayesian Maximum Entropy (BME) method to estimate the extent and the magnitude of the radioactive soil contamination by 137Cs due to the Chernobyl fallout. The powerful BME method allows rigorous incorporation of a wide variety of knowledge bases into the spatial estimation procedure leading to informative contamination maps. Exact measurements (?hard? data) are combined with secondary information on local uncertainties (treated as ?soft? data) to generate science-based uncertainty assessment of soil contamination estimates at unsampled locations. BME describes uncertainty in terms of the posterior probability distributions generated across space, whereas no assumption about the underlying distribution is made and non-linear estimators are automatically incorporated. Traditional estimation variances based on the assumption of an underlying Gaussian distribution (analogous, e.g., to the kriging variance) can be derived as a special case of the BME uncertainty analysis. The BME estimates obtained using hard and soft data are compared with the BME estimates obtained using only hard data. The comparison involves both the accuracy of the estimation maps using the exact data and the assessment of the associated uncertainty using repeated measurements. Furthermore, a comparison of the spatial estimation accuracy obtained by the two methods was carried out using a validation data set of hard data. Finally, a separate uncertainty analysis was conducted that evaluated the ability of the posterior probabilities to reproduce the distribution of the raw repeated measurements available in certain populated sites. The analysis provides an illustration of the improvement in mapping accuracy obtained by adding soft data to the existing hard data and, in general, demonstrates that the BME method performs well both in terms of estimation accuracy as well as in terms estimation error assessment, which are both useful features for the Chernobyl fallout study.

Identification and functional characterization of Rca1, a transcription factor involved in both antifungal susceptibility and host response in Candida albicans.

The identification of novel transcription factors associated with antifungal response may allow the discovery of fungus-specific targets for new therapeutic strategies. A collection of 241 Candida albicans transcriptional regulator mutants was screened for altered susceptibility to fluconazole, caspofungin, amphotericin B, and 5-fluorocytosine. Thirteen of these mutants not yet identified in terms of their role in antifungal response were further investigated, and the function of one of them, a mutant of orf19.6102 (RCA1), was characterized by transcriptome analysis. Strand-specific RNA sequencing and phenotypic tests assigned Rca1 as the regulator of hyphal formation through the cyclic AMP/protein kinase A (cAMP/PKA) signaling pathway and the transcription factor Efg1, but also probably through its interaction with a transcriptional repressor, most likely Tup1. The mechanisms responsible for the high level of resistance to caspofungin and fluconazole observed resulting from RCA1 deletion were investigated. From our observations, we propose that caspofungin resistance was the consequence of the deregulation of cell wall gene expression and that fluconazole resistance was linked to the modulation of the cAMP/PKA signaling pathway activity. In conclusion, our large-scale screening of a C. albicans transcription factor mutant collection allowed the identification of new effectors of the response to antifungals. The functional characterization of Rca1 assigned this transcription factor and its downstream targets as promising candidates for the development of new therapeutic strategies, as Rca1 influences host sensing, hyphal development, and antifungal response.

Mycorrhizal ecology and evolution: the past, the present, and the future.

1406 I. 1407 II. 1408 III. 1410 IV. 1411 V. 1413 VI. 1416 VII. 1418 1418 References 1419 SUMMARY: Almost all land plants form symbiotic associations with mycorrhizal fungi. These below-ground fungi play a key role in terrestrial ecosystems as they regulate nutrient and carbon cycles, and influence soil structure and ecosystem multifunctionality. Up to 80% of plant N and P is provided by mycorrhizal fungi and many plant species depend on these symbionts for growth and survival. Estimates suggest that there are c. 50 000 fungal species that form mycorrhizal associations with c. 250 000 plant species. The development of high-throughput molecular tools has helped us to better understand the biology, evolution, and biodiversity of mycorrhizal associations. Nuclear genome assemblies and gene annotations of 33 mycorrhizal fungal species are now available providing fascinating opportunities to deepen our understanding of the mycorrhizal lifestyle, the metabolic capabilities of these plant symbionts, the molecular dialogue between symbionts, and evolutionary adaptations across a range of mycorrhizal associations. Large-scale molecular surveys have provided novel insights into the diversity, spatial and temporal dynamics of mycorrhizal fungal communities. At the ecological level, network theory makes it possible to analyze interactions between plant-fungal partners as complex underground multi-species networks. Our analysis suggests that nestedness, modularity and specificity of mycorrhizal networks vary and depend on mycorrhizal type. Mechanistic models explaining partner choice, resource exchange, and coevolution in mycorrhizal associations have been developed and are being tested. This review ends with major frontiers for further research.