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.

Applying machine learning methods to avalanche forecasting

Avalanche forecasting is a complex process involving the assimilation of multiple data sources to make predictions over varying spatial and temporal resolutions. Numerically assisted forecasting often uses nearest neighbour methods (NN), which are known to have limitations when dealing with high dimensional data. We apply Support Vector Machines to a dataset from Lochaber, Scotland to assess their applicability in avalanche forecasting. Support Vector Machines (SVMs) belong to a family of theoretically based techniques from machine learning and are designed to deal with high dimensional data. Initial experiments showed that SVMs gave results which were comparable with NN for categorical and probabilistic forecasts. Experiments utilising the ability of SVMs to deal with high dimensionality in producing a spatial forecast show promise, but require further work.

Individual prediction of cognitive decline in mild cognitive impairment using support vector machine-based analysis of diffusion tensor imaging data.

Although cross-sectional diffusion tensor imaging (DTI) studies revealed significant white matter changes in mild cognitive impairment (MCI), the utility of this technique in predicting further cognitive decline is debated. Thirty-five healthy controls (HC) and 67 MCI subjects with DTI baseline data were neuropsychologically assessed at one year. Among them, there were 40 stable (sMCI; 9 single domain amnestic, 7 single domain frontal, 24 multiple domain) and 27 were progressive (pMCI; 7 single domain amnestic, 4 single domain frontal, 16 multiple domain). Fractional anisotropy (FA) and longitudinal, radial, and mean diffusivity were measured using Tract-Based Spatial Statistics. Statistics included group comparisons and individual classification of MCI cases using support vector machines (SVM). FA was significantly higher in HC compared to MCI in a distributed network including the ventral part of the corpus callosum, right temporal and frontal pathways. There were no significant group-level differences between sMCI versus pMCI or between MCI subtypes after correction for multiple comparisons. However, SVM analysis allowed for an individual classification with accuracies up to 91.4% (HC versus MCI) and 98.4% (sMCI versus pMCI). When considering the MCI subgroups separately, the minimum SVM classification accuracy for stable versus progressive cognitive decline was 97.5% in the multiple domain MCI group. SVM analysis of DTI data provided highly accurate individual classification of stable versus progressive MCI regardless of MCI subtype, indicating that this method may become an easily applicable tool for early individual detection of MCI subjects evolving to dementia.

A two-week workshop to promote cardiovascular disease prevention programs in countries with limited resources.

Training is a crucial tool for building the capacity necessary for prevention and control of cardiovascular diseases (CVDs) in developing countries. This paper summarizes some features of a 2-week workshop aimed at enabling local health professionals to initiate a comprehensive CVD prevention and control program in a context of limited resources. The workshops have been organized in the regions where CVD prevention programs are being contemplated, in cooperation with health authorities of the concerned regions. The workshop's content includes a broad variety of issues related to CVD prevention and control, and to program development. Strong emphasis is placed on "learning by doing," and groups of 5-6 participants conduct a small-scale epidemiological study during the first week; during the second week, they draft a virtual program of CVD prevention and control adapted to the local situation. This practice-oriented workshop focuses on building expertise among anticipated key players, strengthening networks among relevant health professionals, and advocating the urgent need to tackle the emerging CVD epidemic in developing countries.

Management of hepatitis C virus (HCV) infection in drug substitution programs.

BACKGROUND: In Switzerland, intravenous drug use (IDU) accounts for 80% of newly acquired hepatitis C virus (HCV) infections. Early HCV treatment has the potential to interrupt the transmission chain and reduce morbidity/mortality due to decompensated liver cirrhosis and hepatocellular carcinoma. Nevertheless, patients in drug substitution programs are often insufficiently screened and treated. OBJECTIVE/METHODS: With the aim to improve HCV management in IDUs, we conducted a cross sectional chart review in three opioid substitution programs in St. Gallen (125 methadone and 71 heroin recipients). Results were compared with another heroin substitution program in Bern (202 patients) and SCCS/SHCS data. RESULTS: Among the methadone/heroin recipients in St. Gallen, diagnostic workup of HCV was better than expected: HCV/HIV-status was unknown in only 1% (2/196), HCV RNA was not performed in 9% (13/146) of anti-HCV-positives and the genotype missing in 15% (12/78) of HCV RNA-positives. In those without spontaneous clearance (two thirds), HCV treatment uptake was 23% (21/91) (HIV-: 29% (20/68), HIV+: 4% (1/23)), which was lower than in methadone/heroin recipients and particularly non-IDUs within the SCCS/SHCS, but higher than in the, mainly psychiatrically focussed, heroin substitution program in Bern (8%). Sustained virological response (SVR) rates were comparable in all settings (overall: 50%, genotype 1: 35-40%, genotype 3: two thirds). In St. Gallen, the median delay from the estimated date of infection (IDU start) to first diagnosis was 10 years and to treatment was another 7.5 years. CONCLUSIONS: Future efforts need to focus on earlier HCV diagnosis and improvement of treatment uptake among patients in drug substitution programs, particularly if patients are HIV-co-infected. New potent drugs might facilitate the decision to initiate treatment.

Why microalgal biofuels won't save the internal combustion machine

Proponents of microalgae biofuel technologies often claim that the world demand of liquid fuels, about 5 trillion liters per year, could be supplied by microalgae cultivated on only a few tens of millions of hectares. This perspective reviews this subject and points out that such projections are greatly exaggerated, because (1) the pro- ductivities achieved in large-scale commercial microalgae production systems, operated year-round, do not surpass those of irrigated tropical crops; (2) cultivating, harvesting and processing microalgae solely for the production of biofuels is simply too expensive using current or prospective technology; and (3) currently available (limited) data suggest that the energy balance of algal biofuels is very poor. Thus, microalgal biofuels are no panacea for depleting oil or global warming, and are unlikely to save the internal combustion machine.

A critical analysis of three psychological research programs of doping behaviour

Objectives To consider the various specific substances-taking activities in sport an examination of three psychological models of doping behaviour utilised by researchers is presented in order to evaluate their real and potential impact, and to improve the relevance and efficiency of anti-doping campaigns. Design Adopting the notion of a "research program" (Lakatos, 1978) from the philosophy of science, a range of studies into the psychology of doping behaviour are classified and critically analysed. Method Theoretical and practical parameters of three research programs are critically evaluated (i) cognitive; (ii) drive; and (iii) situated-dynamic. Results The analysis reveals the diversity of theoretical commitments of the research programs and their practical consequences. The «cognitive program» assumes that athletes are accountable for their acts that reflect the endeavour to attain sporting and non-sporting goals. Attitudes, knowledge and rational decisions are understood to be the basis of doping behaviour. The «drive program» characterises the variety of traces and consequences on psychological and somatic states coming from athlete's experience with sport. Doping behaviour here is conceived of as a solution to reduce unconscious psychological and somatic distress. The «situated-dynamic program» considers a broader context of athletes' doping activity and its evolution during a sport career. Doping is considered as emergent and self-organized behaviour, grounded on temporally critical couplings between athletes' actions and situations and the specific dynamics of their development during the sporting life course. Conclusions These hypothetical, theoretical and methodological considerations offer a more nuanced understanding of doping behaviours, making an effective contribution to anti-doping education and research by enabling researchers and policy personnel to become more critically reflective about their explicit and implicit assumptions regarding models of explanations for doping behaviour.

Data-driven topo-climatic mapping with machine learning methods

Automatic environmental monitoring networks enforced by wireless communication technologies provide large and ever increasing volumes of data nowadays. The use of this information in natural hazard research is an important issue. Particularly useful for risk assessment and decision making are the spatial maps of hazard-related parameters produced from point observations and available auxiliary information. The purpose of this article is to present and explore the appropriate tools to process large amounts of available data and produce predictions at fine spatial scales. These are the algorithms of machine learning, which are aimed at non-parametric robust modelling of non-linear dependencies from empirical data. The computational efficiency of the data-driven methods allows producing the prediction maps in real time which makes them superior to physical models for the operational use in risk assessment and mitigation. Particularly, this situation encounters in spatial prediction of climatic variables (topo-climatic mapping). In complex topographies of the mountainous regions, the meteorological processes are highly influenced by the relief. The article shows how these relations, possibly regionalized and non-linear, can be modelled from data using the information from digital elevation models. The particular illustration of the developed methodology concerns the mapping of temperatures (including the situations of Föhn and temperature inversion) given the measurements taken from the Swiss meteorological monitoring network. The range of the methods used in the study includes data-driven feature selection, support vector algorithms and artificial neural networks.

Machine learning for spatial environmental data. Theory, applications and software

The book presents the state of the art in machine learning algorithms (artificial neural networks of different architectures, support vector machines, etc.) as applied to the classification and mapping of spatially distributed environmental data. Basic geostatistical algorithms are presented as well. New trends in machine learning and their application to spatial data are given, and real case studies based on environmental and pollution data are carried out. The book provides a CD-ROM with the Machine Learning Office software, including sample sets of data, that will allow both students and researchers to put the concepts rapidly to practice.

Two-step postmortem angiography with a modified heart-lung machine: preliminary results.

OBJECTIVE: The purpose of this study was to adapt and improve a minimally invasive two-step postmortem angiographic technique for use on human cadavers. Detailed mapping of the entire vascular system is almost impossible with conventional autopsy tools. The technique described should be valuable in the diagnosis of vascular abnormalities. MATERIALS AND METHODS: Postmortem perfusion with an oily liquid is established with a circulation machine. An oily contrast agent is introduced as a bolus injection, and radiographic imaging is performed. In this pilot study, the upper or lower extremities of four human cadavers were perfused. In two cases, the vascular system of a lower extremity was visualized with anterograde perfusion of the arteries. In the other two cases, in which the suspected cause of death was drug intoxication, the veins of an upper extremity were visualized with retrograde perfusion of the venous system. RESULTS: In each case, the vascular system was visualized up to the level of the small supplying and draining vessels. In three of the four cases, vascular abnormalities were found. In one instance, a venous injection mark engendered by the self-administration of drugs was rendered visible by exudation of the contrast agent. In the other two cases, occlusion of the arteries and veins was apparent. CONCLUSION: The method described is readily applicable to human cadavers. After establishment of postmortem perfusion with paraffin oil and injection of the oily contrast agent, the vascular system can be investigated in detail and vascular abnormalities rendered visible.