Multiple Kernel Learning of Environmental Data. Case study: Analysis and Mapping of Wind Fields

The paper presents the Multiple Kernel Learning (MKL) approach as a modelling and data exploratory tool and applies it to the problem of wind speed mapping. Support Vector Regression (SVR) is used to predict spatial variations of the mean wind speed from terrain features (slopes, terrain curvature, directional derivatives) generated at different spatial scales. Multiple Kernel Learning is applied to learn kernels for individual features and thematic feature subsets, both in the context of feature selection and optimal parameters determination. An empirical study on real-life data confirms the usefulness of MKL as a tool that enhances the interpretability of data-driven models.

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.

Enantioselective transformation of alpha-hexachlorocyclohexane by the dehydrochlorinases LinA1 and LinA2 from the soil bacterium Sphingomonas paucimobilis B90A.

Sphingomonas paucimobilis B90A contains two variants, LinA1 and LinA2, of a dehydrochlorinase that catalyzes the first and second steps in the metabolism of hexachlorocyclohexanes (R. Kumari, S. Subudhi, M. Suar, G. Dhingra, V. Raina, C. Dogra, S. Lal, J. R. van der Meer, C. Holliger, and R. Lal, Appl. Environ. Microbiol. 68:6021-6028, 2002). On the amino acid level, LinA1 and LinA2 were 88% identical to each other, and LinA2 was 100% identical to LinA of S. paucimobilis UT26. Incubation of chiral alpha-hexachlorocyclohexane (alpha-HCH) with Escherichia coli BL21 expressing functional LinA1 and LinA2 S-glutathione transferase fusion proteins showed that LinA1 preferentially converted the (+) enantiomer, whereas LinA2 preferred the (-) enantiomer. Concurrent formation and subsequent dissipation of beta-pentachlorocyclohexene enantiomers was also observed in these experiments, indicating that there was enantioselective formation and/or dissipation of these enantiomers. LinA1 preferentially formed (3S,4S,5R,6R)-1,3,4,5,6-pentachlorocyclohexene, and LinA2 preferentially formed (3R,4R,5S,6S)-1,3,4,5,6-pentachlorocyclohexene. Because enantioselectivity was not observed in incubations with whole cells of S. paucimobilis B90A, we concluded that LinA1 and LinA2 are equally active in this organism. The enantioselective transformation of chiral alpha-HCH by LinA1 and LinA2 provides the first evidence of the molecular basis for the changed enantiomer composition of alpha-HCH in many natural environments. Enantioselective degradation may be one of the key processes determining enantiomer composition, especially when strains that contain only one of the linA genes, such as S. paucimobilis UT26, prevail.

Soil tillage affects the community structure of mycorrhizal fungi in maize roots

In this study we tested whether communities of arbuscular mycorrhizal fungi (AMF) colonizing the roots of maize (Zea mays L.) were affected by soil tillage practices (plowing, chiseling, and no-till) in a long-term field experiment carried out in Tanikon (Switzerland). AMF were identified in the roots using specific polymerase chain reaction (PCR) markers that had been developed for the AMF previously isolated from the soils of the studied site. A nested PCR procedure with primers of increased specificity (eukaryotic, then, fungal, then AMF species or. species-grouop specific) was used. Sequencing of amplified DNA confirmed that the DNA obtained from the maize roots was of AMF origin. Presence of particular AMF species or species-group was scored as a presence of a DNA product after PCR with specific primers. We also used single-strand conformation polymorphism analysis (SSCP), of amplified DNA samples to-check if the amplification of the DNA from maize roots matched the expected profile for a particular AMF isolate with a given specific primer pair. Presence of the genus Scutellospora, in maize roots was strongly reduced in plowed and chiseled soils. Fungi from the suborder Glomineae were more prevalent colonizers of maize roots growing in plowed soils, but were also present in the roots from other tillage treatments. These changes in community of AMF colonizing maize roots might be due to (1), the differences in tolerance to the tillage-induced disruption of the hyphae among the different AMF species, (2) changes in nutrient content of the soil, (3) changes in microbial activity, or (4) changes in weed populations in response to soil tillage. This is the first report on community composition of AMF in the roots of a field-grown crop plant (maize) as affected by soil tillage.

Speciation of naturally-accumulated uranium in an organic-rich soil of an alpine region (Switzerland)

Very high concentrations of uranium (up to 4000 ppm) were found in a natural soil in the Dischma valley, an alpine region in the Grisons canton in Switzerland. The goal of this study was to examine the redox state and the nature of uranium binding in the soil matrix in order to understand the accumulation mechanism. Pore water profiles collected from Dischma soil revealed the establishment of anoxic conditions with increasing soil depth. A combination of chemical extraction methods and spectroscopy was applied to characterize the redox state and binding environment of uranium in the soil. Bicarbonate extraction under anoxic conditions released most of the uranium indicating that uranium occurs predominantly in the hexavalent form. Surprisingly, the uranium redox state did not vary greatly as a function of depth. X-ray absorption near edge spectroscopy (XANES), confirmed that uranium was present as a mixture of U(VI) and U(IV) with U(VI) dominating. Sequential extractions of soil samples showed that the dissolution of solid organic matter resulted in the simultaneous release of the majority of the soil uranium content (>95%). Extended X-ray absorption fine structure (EXAFS) spectroscopy also revealed that soil-associated uranium in the soil matrix was mainly octahedrally coordinated, with an average of 1.7 axial (at about 1.76 Å) and 4.6 to 5.3 equatorial oxygen atoms (at about 2.36 Å) indicating the dominance of a uranyl-like (UO22+) structure presumably mixed with some U(IV). An additional EXAFS signal (at about 3.2 Å) identified in some spectra suggested that uranium was also bound (via an oxygen atom) to a light element such as carbon, phosphorus or silicon. Gamma spectrometric measurements of soil profiles failed to identify uranium long-life daughter products in the soil which is an indication that uranium originates elsewhere and was transported to its current location by water. Finally, it was found that the release of uranium from the soil was significantly promoted at very low pH values (pH 2) and increased with increasing pH values (between pH 5 and 9).

cDNA cloning and mapping of a novel islet-brain/JNK-interacting protein.

IB1/JIP-1 is a scaffold protein that regulates the c-Jun NH(2)-terminal kinase (JNK) signaling pathway, which is activated by environmental stresses and/or by treatment with proinflammatory cytokines including IL-1beta and TNF-alpha. The JNKs play an essential role in many biological processes, including the maturation and differentiation of immune cells and the apoptosis of cell targets of the immune system. IB1 is expressed predominantly in brain and pancreatic beta-cells where it protects cells from proapoptotic programs. Recently, a mutation in the amino-terminus of IB1 was associated with diabetes. A novel isoform, IB2, was cloned and characterized. Overall, both IB1 and IB2 proteins share a very similar organization, with a JNK-binding domain, a Src homology 3 domain, a phosphotyrosine-interacting domain, and polyacidic and polyproline stretches located at similar positions. The IB2 gene (HGMW-approved symbol MAPK8IP2) maps to human chromosome 22q13 and contains 10 coding exons. Northern and RT-PCR analyses indicate that IB2 is expressed in brain and in pancreatic cells, including insulin-secreting cells. IB2 interacts with both JNK and the JNK-kinase MKK7. In addition, ectopic expression of the JNK-binding domain of IB2 decreases IL-1beta-induced pancreatic beta-cell death. These data establish IB2 as a novel scaffold protein that regulates the JNK signaling pathway in brain and pancreatic beta-cells and indicate that IB2 represents a novel candidate gene for diabetes.

Evaluating effects of spectral training data distribution on continuous field mapping performance

Continuous field mapping has to address two conflicting remote sensing requirements when collecting training data. On one hand, continuous field mapping trains fractional land cover and thus favours mixed training pixels. On the other hand, the spectral signature has to be preferably distinct and thus favours pure training pixels. The aim of this study was to evaluate the sensitivity of training data distribution along fractional and spectral gradients on the resulting mapping performance. We derived four continuous fields (tree, shrubherb, bare, water) from aerial photographs as response variables and processed corresponding spectral signatures from multitemporal Landsat 5 TM data as explanatory variables. Subsequent controlled experiments along fractional cover gradients were then based on generalised linear models. Resulting fractional and spectral distribution differed between single continuous fields, but could be satisfactorily trained and mapped. Pixels with fractional or without respective cover were much more critical than pure full cover pixels. Error distribution of continuous field models was non-uniform with respect to horizontal and vertical spatial distribution of target fields. We conclude that a sampling for continuous field training data should be based on extent and densities in the fractional and spectral, rather than the real spatial space. Consequently, adequate training plots are most probably not systematically distributed in the real spatial space, but cover the gradient and covariate structure of the fractional and spectral space well. (C) 2009 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

Effects of long-term irrigation with treated wastewater. Part I: Evolution of soil physico-chemical properties

The long-term impact of irrigation on a Mediterranean sandy soil irrigated with Treated wastewater (TWW) since 1980 was evaluated. The main soil properties (CEC, pH, size distribution, exchangeable cations and chloride, hydraulic conductivity) as well as the organic matter and Cu, Cr and Pb speciation in an irrigated soil and a non-irrigated control soil at various soil depths were monitored and compared during a 2 years experiment. In this first part, the evolution of the physico-chemical soil properties was described. The irrigation with TWW was beneficial with regard to water and nutrient supplying. All the exchangeable cations other than K(+) were higher in the irrigated soil than in the reference one. A part of the exchangeable cations was not fixed on the exchange complex but stored as labile salts or in concentrated soil solution. Despite the very sandy soil texture, both saturated and unsaturated hydraulic conductivity exhibited a significant diminution in the irrigated soil, but remained high enough to allow water percolation during rainy periods and subsequent leaching of accumulated salts, preventing soil salinization. In the irrigated soil, exchangeable sodium percentage (ESP) exhibited high values (20% on average) and the soil organic C was lower than in the reference. No significant effect was noticed on soil mineralogical composition due to irrigation. (C) 2010 Published by Elsevier Ltd.