Reduction capability of soil humic substances from the Rio Negro basin, Brazil, towards Hg(II) studied by a multimethod approach and principal component analysis (PCA)

This paper characterizes humic substances (HS) extracted from soil samples collected in the Rio Negro basin in the state of Amazonas, Brazil, particularly investigating their reduction capabilities towards Hg(II) in order to elucidate potential mercury cycling/volatilization in this environment. For this reason, a multimethod approach was used, consisting of both instrumental methods (elemental analysis, EPR, solid-state NMR, FIA combined with cold-vapor AAS of Hg(0)) and statistical methods such as principal component analysis (PCA) and a central composite factorial planning method. The HS under study were divided into groups, complexing and reducing ones, owing to different distribution of their functionalities. The main functionalities (cor)related with reduction of Hg(II) were phenolic, carboxylic and amide groups, while the groups related with complexation of Hg(II) were ethers, hydroxyls, aldehydes and ketones. The HS extracted from floodable regions of the Rio Negro basin presented a greater capacity to retain (to complex, to adsorb physically and/or chemically) Hg(II), while nonfloodable regions showed a greater capacity to reduce Hg(II), indicating that HS extracted from different types of regions contribute in different ways to the biogeochemical mercury cycle in the basin of the mid-Rio Negro, AM, Brazil. (c) 2007 Published by Elsevier B.V.

Identification and Phylogenetic Analysis of Heme Synthesis Genes in Trypanosomatids and Their Bacterial Endosymbionts

It has been known for decades that some insect-infecting trypanosomatids can survive in culture without heme supplementation while others cannot, and that this capability is associated with the presence of a betaproteobacterial endosymbiont in the flagellate's cytoplasm. However, the specific mechanisms involved in this process remained obscure. In this work, we sequence and phylogenetically analyze the heme pathway genes from the symbionts and from their hosts, as well as from a number of heme synthesis-deficient Kinetoplastida. Our results show that the enzymes responsible for synthesis of heme are encoded on the symbiont genomes and produced in close cooperation with the flagellate host. Our evidence suggests that this synergistic relationship is the end result of a history of extensive gene loss and multiple lateral gene transfer events in different branches of the phylogeny of the Trypanosomatidae.

Leak detection by inverse transient analysis in an experimental PVC pipe system

Leakage reduction in water supply systems and distribution networks has been an increasingly important issue in the water industry since leaks and ruptures result in major physical and economic losses. Hydraulic transient solvers can be used in the system operational diagnosis, namely for leak detection purposes, due to their capability to describe the dynamic behaviour of the systems and to provide substantial amounts of data. In this research work, the association of hydraulic transient analysis with an optimisation model, through inverse transient analysis (ITA), has been used for leak detection and its location in an experimental facility containing PVC pipes. Observed transient pressure data have been used for testing ITA. A key factor for the success of the leak detection technique used is the accurate calibration of the transient solver, namely adequate boundary conditions and the description of energy dissipation effects since PVC pipes are characterised by a viscoelastic mechanical response. Results have shown that leaks were located with an accuracy between 4-15% of the total length of the pipeline, depending on the discretisation of the system model.

Process Capability Indexes Determination on Tabletting Performance during the Process Validation for Metamizol Tablets

The aim of the present study was to provide a numerical measure, through the process capability indexes (PCIs), C(p) and C(pk), on whether or not the manufacturing process can be considered capable of producing metamizol (500 mg) tablets. They were also used as statistical tool in order to prove the consistency of the tabletting process, making sure that the tablet weight and the content uniformity of metamizol are able to comply with the preset requirements. Besides that, the ANOVA, the t-test and the test for equal variances were applied to this study, allowing additional knowledge of the tabletting phase. Therefore, the proposed statistical approach intended to assure more safety, precision and accuracy on the process validation analysis.

Forward-looking versus recursive-dynamic modeling in climate policy analysis: A comparison

This paper develops a multi-regional general equilibrium model for climate policy analysis based on the latest version of the MIT Emissions Prediction and Policy Analysis (EPPA) model. We develop two versions so that we can solve the model either as a fully inter-temporal optimization problem (forward-looking, perfect foresight) or recursively. The standard EPPA model on which these models are based is solved recursively, and it is necessary to simplify some aspects of it to make inter-temporal solution possible. The forward-looking capability allows one to better address economic and policy issues such as borrowing and banking of GHG allowances, efficiency implications of environmental tax recycling, endogenous depletion of fossil resources, international capital flows, and optimal emissions abatement paths among others. To evaluate the solution approaches, we benchmark each version to the same macroeconomic path, and then compare the behavior of the two versions under a climate policy that restricts greenhouse gas emissions. We find that the energy sector and CO(2) price behavior are similar in both versions (in the recursive version of the model we force the inter-temporal theoretical efficiency result that abatement through time should be allocated such that the CO(2) price rises at the interest rate.) The main difference that arises is that the macroeconomic costs are substantially lower in the forward-looking version of the model, since it allows consumption shifting as an additional avenue of adjustment to the policy. On the other hand, the simplifications required for solving the model as an optimization problem, such as dropping the full vintaging of the capital stock and fewer explicit technological options, likely have effects on the results. Moreover, inter-temporal optimization with perfect foresight poorly represents the real economy where agents face high levels of uncertainty that likely lead to higher costs than if they knew the future with certainty. We conclude that while the forward-looking model has value for some problems, the recursive model produces similar behavior in the energy sector and provides greater flexibility in the details of the system that can be represented. (C) 2009 Elsevier B.V. All rights reserved.

On-line analysis of froth surface in coal and mineral flotation using JKFrothCam

The personal computer revolution has resulted in the widespread availability of low-cost image analysis hardware. At the same time, new graphic file formats have made it possible to handle and display images at resolutions beyond the capability of the human eye. Consequently, there has been a significant research effort in recent years aimed at making use of these hardware and software technologies for flotation plant monitoring. Computer-based vision technology is now moving out of the research laboratory and into the plant to become a useful means of monitoring and controlling flotation performance at the cell level. This paper discusses the metallurgical parameters that influence surface froth appearance and examines the progress that has been made in image analysis of flotation froths. The texture spectrum and pixel tracing techniques developed at the Julius Kruttschnitt Mineral Research Centre are described in detail. The commercial implementation, JKFrothCam, is one of a number of froth image analysis systems now reaching maturity. In plants where it is installed, JKFrothCam has shown a number of performance benefits. Flotation runs more consistently, meeting product specifications while maintaining high recoveries. The system has also shown secondary benefits in that reagent costs have been significantly reduced as a result of improved flotation control. (C) 2002 Elsevier Science B.V. All rights reserved.

Non-volatile analysis in fruits by laser resonant ionization spectrometry: application to resveratrol (3,5,4 -trihydroxystilbene) in grapes

Applied Physics B Lasers and Optics, vol.71

Unmixing hyperspectral data: independent and dependent component analysis

The development of high spatial resolution airborne and spaceborne sensors has improved the capability of ground-based data collection in the fields of agriculture, geography, geology, mineral identification, detection [2, 3], and classification [4–8]. The signal read by the sensor from a given spatial element of resolution and at a given spectral band is a mixing of components originated by the constituent substances, termed endmembers, located at that element of resolution. This chapter addresses hyperspectral unmixing, which is the decomposition of the pixel spectra into a collection of constituent spectra, or spectral signatures, and their corresponding fractional abundances indicating the proportion of each endmember present in the pixel [9, 10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. The linear mixing model holds when the mixing scale is macroscopic [13]. The nonlinear model holds when the mixing scale is microscopic (i.e., intimate mixtures) [14, 15]. The linear model assumes negligible interaction among distinct endmembers [16, 17]. The nonlinear model assumes that incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [18]. Under the linear mixing model and assuming that the number of endmembers and their spectral signatures are known, hyperspectral unmixing is a linear problem, which can be addressed, for example, under the maximum likelihood setup [19], the constrained least-squares approach [20], the spectral signature matching [21], the spectral angle mapper [22], and the subspace projection methods [20, 23, 24]. Orthogonal subspace projection [23] reduces the data dimensionality, suppresses undesired spectral signatures, and detects the presence of a spectral signature of interest. The basic concept is to project each pixel onto a subspace that is orthogonal to the undesired signatures. As shown in Settle [19], the orthogonal subspace projection technique is equivalent to the maximum likelihood estimator. This projection technique was extended by three unconstrained least-squares approaches [24] (signature space orthogonal projection, oblique subspace projection, target signature space orthogonal projection). Other works using maximum a posteriori probability (MAP) framework [25] and projection pursuit [26, 27] have also been applied to hyperspectral data. In most cases the number of endmembers and their signatures are not known. Independent component analysis (ICA) is an unsupervised source separation process that has been applied with success to blind source separation, to feature extraction, and to unsupervised recognition [28, 29]. ICA consists in finding a linear decomposition of observed data yielding statistically independent components. Given that hyperspectral data are, in given circumstances, linear mixtures, ICA comes to mind as a possible tool to unmix this class of data. In fact, the application of ICA to hyperspectral data has been proposed in reference 30, where endmember signatures are treated as sources and the mixing matrix is composed by the abundance fractions, and in references 9, 25, and 31–38, where sources are the abundance fractions of each endmember. In the first approach, we face two problems: (1) The number of samples are limited to the number of channels and (2) the process of pixel selection, playing the role of mixed sources, is not straightforward. In the second approach, ICA is based on the assumption of mutually independent sources, which is not the case of hyperspectral data, since the sum of the abundance fractions is constant, implying dependence among abundances. This dependence compromises ICA applicability to hyperspectral images. In addition, hyperspectral data are immersed in noise, which degrades the ICA performance. IFA [39] was introduced as a method for recovering independent hidden sources from their observed noisy mixtures. IFA implements two steps. First, source densities and noise covariance are estimated from the observed data by maximum likelihood. Second, sources are reconstructed by an optimal nonlinear estimator. Although IFA is a well-suited technique to unmix independent sources under noisy observations, the dependence among abundance fractions in hyperspectral imagery compromises, as in the ICA case, the IFA performance. Considering the linear mixing model, hyperspectral observations are in a simplex whose vertices correspond to the endmembers. Several approaches [40–43] have exploited this geometric feature of hyperspectral mixtures [42]. Minimum volume transform (MVT) algorithm [43] determines the simplex of minimum volume containing the data. The MVT-type approaches are complex from the computational point of view. Usually, these algorithms first find the convex hull defined by the observed data and then fit a minimum volume simplex to it. Aiming at a lower computational complexity, some algorithms such as the vertex component analysis (VCA) [44], the pixel purity index (PPI) [42], and the N-FINDR [45] still find the minimum volume simplex containing the data cloud, but they assume the presence in the data of at least one pure pixel of each endmember. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. Hyperspectral sensors collects spatial images over many narrow contiguous bands, yielding large amounts of data. For this reason, very often, the processing of hyperspectral data, included unmixing, is preceded by a dimensionality reduction step to reduce computational complexity and to improve the signal-to-noise ratio (SNR). Principal component analysis (PCA) [46], maximum noise fraction (MNF) [47], and singular value decomposition (SVD) [48] are three well-known projection techniques widely used in remote sensing in general and in unmixing in particular. The newly introduced method [49] exploits the structure of hyperspectral mixtures, namely the fact that spectral vectors are nonnegative. The computational complexity associated with these techniques is an obstacle to real-time implementations. To overcome this problem, band selection [50] and non-statistical [51] algorithms have been introduced. This chapter addresses hyperspectral data source dependence and its impact on ICA and IFA performances. The study consider simulated and real data and is based on mutual information minimization. Hyperspectral observations are described by a generative model. This model takes into account the degradation mechanisms normally found in hyperspectral applications—namely, signature variability [52–54], abundance constraints, topography modulation, and system noise. The computation of mutual information is based on fitting mixtures of Gaussians (MOG) to data. The MOG parameters (number of components, means, covariances, and weights) are inferred using the minimum description length (MDL) based algorithm [55]. We study the behavior of the mutual information as a function of the unmixing matrix. The conclusion is that the unmixing matrix minimizing the mutual information might be very far from the true one. Nevertheless, some abundance fractions might be well separated, mainly in the presence of strong signature variability, a large number of endmembers, and high SNR. We end this chapter by sketching a new methodology to blindly unmix hyperspectral data, where abundance fractions are modeled as a mixture of Dirichlet sources. This model enforces positivity and constant sum sources (full additivity) constraints. The mixing matrix is inferred by an expectation-maximization (EM)-type algorithm. This approach is in the vein of references 39 and 56, replacing independent sources represented by MOG with mixture of Dirichlet sources. Compared with the geometric-based approaches, the advantage of this model is that there is no need to have pure pixels in the observations. The chapter is organized as follows. Section 6.2 presents a spectral radiance model and formulates the spectral unmixing as a linear problem accounting for abundance constraints, signature variability, topography modulation, and system noise. Section 6.3 presents a brief resume of ICA and IFA algorithms. Section 6.4 illustrates the performance of IFA and of some well-known ICA algorithms with experimental data. Section 6.5 studies the ICA and IFA limitations in unmixing hyperspectral data. Section 6.6 presents results of ICA based on real data. Section 6.7 describes the new blind unmixing scheme and some illustrative examples. Section 6.8 concludes with some remarks.

Biocontrol strain Pseudomonas fluorescens CHA0 and its genetically modified derivative with enhanced biocontrol capability exert comparable effects on the structure of a Sinorhizobium meliloti population in a gnotobiotic system

The impact of biocontrol strain Pseudomonas fluorescens CHA0 and of its genetically modified, antibiotic-overproducing derivative CHA0/pME3424 on a reconstructed population of the plant-beneficial Sinorhizobium meliloti bacteria was assessed in gnotobiotic systems. In sterile soil, the final density of the reconstructed S. meliloti population decreased by more than one order of magnitude in the presence of either of the Pseudomonas strains when compared to a control without addition of P. fluorescens. Moreover, there was a change in the proportion of each individual S. meliloti strain within the population. Plant tests also revealed changes in the nodulating S. meliloti population in the presence of strains CHA0 or CHA0/pME3424. In both treatments one S. meliloti strain, f43, was significantly reduced in its root nodule occupancy. Analysis of alfalfa yields showed a slight but statistically significant increase in shoot dry weight when strain CHA0 was added to the reconstructed S. meliloti population whereas no such effect was observed with CHA0/pME3424.

Detection rate of FNA cytology in medullary thyroid carcinoma: a meta-analysis.

BACKGROUND: The early detection of medullary thyroid carcinoma (MTC) can improve patient prognosis, because histological stage and patient age at diagnosis are highly relevant prognostic factors. As a consequence, delay in the diagnosis and/or incomplete surgical treatment should correlate with a poorer prognosis for patients. Few papers have evaluated the specific capability of fine-needle aspiration cytology (FNAC) to detect MTC, and small series have been reported. This study conducts a meta-analysis of published data on the diagnostic performance of FNAC in MTC to provide more robust estimates. RESEARCH DESIGN AND METHODS: A comprehensive computer literature search of the PubMed/MEDLINE, Embase and Scopus databases was conducted by searching for the terms 'medullary thyroid' AND 'cytology', 'FNA', 'FNAB', 'FNAC', 'fine needle' or 'fine-needle'. The search was updated until 21 March 2014, and no language restrictions were used. RESULTS: Fifteen relevant studies and 641 MTC lesions that had undergone FNAC were included. The detection rate (DR) of FNAC in patients with MTC (diagnosed as 'MTC' or 'suspicious for MTC') on a per lesion-based analysis ranged from 12·5% to 88·2%, with a pooled estimate of 56·4% (95% CI: 52·6-60·1%). The included studies were statistically heterogeneous in their estimates of DR (I-square >50%). Egger's regression intercept for DR pooling was 0·03 (95% CI: -3·1 to 3·2, P = 0·9). The study that reported the largest MTC series had a DR of 45%. Data on immunohistochemistry for calcitonin in diagnosing MTC were inconsistent for the meta-analysis. CONCLUSIONS: The presented meta-analysis demonstrates that FNAC is able to detect approximately one-half of MTC lesions. These findings suggest that other techniques may be needed in combination with FNAC to diagnose MTC and avoid false negative results.

Mutation analysis of the ATP7B gene in a new group of Wilson's disease patients: contribution to diagnosis.

Wilson's disease (WD), an autosomal recessive disorder of copper transport with a broad range of genotypic and phenotypic characteristics, results from mutations in the ATP7B gene. Herein we report the results of mutation analysis of the ATP7B gene in a group of 118 Wilson disease families (236 chromosomes) prevalently of Italian origin. Using DNA sequencing we identified 83 disease-causing mutations. Eleven were novel, while twenty one already described mutations were identified in new populations in this study. In particular, mutation analysis of 13 families of Romanian origin showed a high prevalence of the p.H1069Q mutation (50%). Detection of new mutations in the ATP7B gene in new populations increases our capability of molecular analysis that is essential for early diagnosis and treatment of WD.

Functional analysis of Ectodysplasin-A mutations causing selective tooth agenesis.

Mutations of the Ectodysplasin-A (EDA) gene are generally associated with the syndrome hypohidrotic ectodermal dysplasia (MIM 305100), but they can also manifest as selective, non-syndromic tooth agenesis (MIM300606). We have performed an in vitro functional analysis of six selective tooth agenesis-causing EDA mutations (one novel and five known) that are located in the C-terminal tumor necrosis factor homology domain of the protein. Our study reveals that expression, receptor binding or signaling capability of the mutant EDA1 proteins is only impaired in contrast to syndrome-causing mutations, which we have previously shown to abolish EDA1 expression, receptor binding or signaling. Our results support a model in which the development of the human dentition, especially of anterior teeth, requires the highest level of EDA-receptor signaling, whereas other ectodermal appendages, including posterior teeth, have less stringent requirements and form normally in response to EDA mutations with reduced activity.

Statistical analysis and modelling of weather radar beam propagation conditions in the Po Valley (Italy)

Ground clutter caused by anomalous propagation (anaprop) can affect seriously radar rain rate estimates, particularly in fully automatic radar processing systems, and, if not filtered, can produce frequent false alarms. A statistical study of anomalous propagation detected from two operational C-band radars in the northern Italian region of Emilia Romagna is discussed, paying particular attention to its diurnal and seasonal variability. The analysis shows a high incidence of anaprop in summer, mainly in the morning and evening, due to the humid and hot summer climate of the Po Valley, particularly in the coastal zone. Thereafter, a comparison between different techniques and datasets to retrieve the vertical profile of the refractive index gradient in the boundary layer is also presented. In particular, their capability to detect anomalous propagation conditions is compared. Furthermore, beam path trajectories are simulated using a multilayer ray-tracing model and the influence of the propagation conditions on the beam trajectory and shape is examined. High resolution radiosounding data are identified as the best available dataset to reproduce accurately the local propagation conditions, while lower resolution standard TEMP data suffers from interpolation degradation and Numerical Weather Prediction model data (Lokal Model) are able to retrieve a tendency to superrefraction but not to detect ducting conditions. Observing the ray tracing of the centre, lower and upper limits of the radar antenna 3-dB half-power main beam lobe it is concluded that ducting layers produce a change in the measured volume and in the power distribution that can lead to an additional error in the reflectivity estimate and, subsequently, in the estimated rainfall rate.

Analysis of the mechanisms controlling the activity of flp1, a chromosomal passenger protein in the fission Schizosaccharomyces pombe

ABSTRACT In S. cerevisiae, the protein phosphatase Cdc14pwt is essential far mitotic exit through its contribution to reducing mitotic CDK activity. But Cdc14pwt also acts as a mare general temporal coordinator of mid and late mitotic events by controlling the partitioning of DNA, microtubule stability and cytokinesis. Cdc14pwt orthologs are well conserved from yeasts to humans, and sequence comparison revealed the presence of three domains, A, B and C, of which A and B form the catalytic domain. Cdc14pwt orthologs are regulated (in part) through cell cycle dependent changes in their localization. Some of them are thought to be kept inactive by sequestration in the nucleolus during interphase. This is the case for flp1pwt, the single identified Cdc14pwt ortholog in the fission yeast S. pombe. In early mitosis, flp1pwt leaves the nucleolus and localizes to the kinetochores, the contractile ring and the mitotic spindle, suggesting that it has multiple substrates and regulates many mitotic processes. flp1D cells show a high chromosome loss rate and septation defects, suggesting a role for flp1wt in the fidelity of chromosome transmission and cytokinesis. The aim of this study is to characterize the mechanisms underlying flp1pwt functions and the control of its activity. A structure-function analysis has revealed that the presence of both A and B domains is required for biological function and for proper flp1pwt mitotic localization. In contrast, the C domain of flp1pwt is responsible for its proper nucleolar localization in G2/interphase. My data suggest that dephosphorylation of substrates by flp1pwt is not necessary for any changes in localization of flp1pwt except that at the medial ring. In that particular case, the catalytic activity of flp1pwt is required for efficient localization, therefore revealing an additional level of regulation. All the functions of flp1pwt assayed to date require its catalytic activity, emphasizing the importance of further identification of its substrates. As described for other orthologs, the capability of selfinteraction and phosphorylation status might help to control flp1pwt activity. My data suggest that flp1pwt forms oligomers in vivo and that phosphorylation is not essential far localization changes of the protein. In addition, the hypophosphorylated form of flp1pwt might be specifically involved in the promotion of cytokinesis. The results of this study suggest that multiple modes of regulation including localization, selfassociation and phosphorylation allow a fine-tuning regulation of flp1pwt phosphatase activity, and more generally that of Cdc14pwt family of phosphatases. RESUME Chez la levure S. cerevisiae, la protéine phosphatase Cdc14pwt est essentielle pour la sortie de mitose du fait de sa contribution dans la réduction d'activité des CDK mitotiques. Comme elle contrôle également le partage de l'ADN, la stabilité des microtubules et la cytokinèse, Cdc14pwt est en fait considérée comme un coordinateur temporel général des évènements de milieu et de fin de mitose. Les orthologues de Cdc14pwt sont bien conservés, des levures jusqu'à l'espèce humaine. Des comparaisons de séquence ont révélé la présence de trois domaines A, B et C, les deux premiers constituant le domaine catalytique. Ils sont régulés (en partie) via des changements dans leur localisation, eux-mêmes dépendants du cycle cellulaire. Plusieurs de ces orthologues sont supposés inactivés par séquestration dans le nucléole en interphase, ce qui est le cas de flp1pwt le seul orthologue de Cdc14pwt identifié chez la levure fissipare S, pombe. En début de mitose, flp1pwt quitte le nucléole et localise au niveau des kinetochores, de l'anneau contractile d'actine et du fuseau mitotique, ce qui laisse supposer de multiples substrats et fonctions. Comme les cellules délétées pour le gène flp1wt présentent un taux élevé de perte de chromosome et des défauts de septation, flp1pwt semble jouer un rôle dans la fidélité de la transmission du matériel génétique et la cytokinèse. Le but de cette étude est de caractériser les mécanismes impliqués dans les fonctions assurées par flp1pwt d'une part, et dans le contrôle de son activité d'autre part. Une analyse structure-fonction a révélé que la présence simultanée des deux domaines A et B est requise pour la fonction biologique de flp1pwt et sa localisation correcte pendant la mitose. Par contre, le domaine C de flp1pwt confère une localisation nucléolaire adéquate en G2/interphase. Mes données suggèrent que la déphosphorylation de substrats par flp1pwt est dispensable pour sa localisation correcte excepté celle à l'anneau médian, qui requiert dans ce cas, l'activité catalytique de flp1pwt, révélant ainsi un niveau de régulation supplémentaire. Toutes les fonctions de flp1 pwt testées jusqu'à présent nécessitent également son activité catalytique, ce qui accentue l'importance de l'identification future de ses substrats. Comme cela a déjà été décrit pour d'autres orthologues, la capacité d'auto-intéraction et le niveau de phosphorylation pourraient contrôler l'activité de flp1pwt. En effet, mes données suggèrent que flp1pwt forme des oligomères in vivo et que la phosphorylation n'est pas essentielle pour les changements de localisation observés pour la protéine. De plus, la forme hypophosphorylée de flp1pwt pourrait être spécifiquement impliquée dans la promotion de la cytokinèse. De multiples modes de régulation incluant la localisation, l'auto-association et la phosphorylation semblent permettre un contrôle fin et subtil de l'activité de la phosphatase flp1pwt, et plus généralement celle des protéines de la famille de Cdc14pwt.

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.