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.

La mortalité par tumeur maligne chez les ouvriers d'une fabrique de caoutchouc en Suisse: étude épidémiologique, 1955-1975. [Cancer mortality among the workers of a Swiss rubber goods factory. Epidemiological study, 1955-75].

In an epidemiologic investigation of mortality among workers in a Swiss rubber-goods factory the cancer mortality in the period 1955-1975 has been studied in all male workers active on 1 January 1955 in (a) a rubber-goods factory and (b) a munitions factory, the latter as reference population. The two groups numbered some 1000 each. Both factories were located in the same Central Swiss village where no other industry was present. Mortality in each industry is compared with that in the Swiss population in general (SMR) and the mortalities of the two industries are compared with each other. The results tend to confirm that rubber workers are exposed to a higher risk of cancer mortality. Three particular types of cancer are briefly discussed: cancer of the stomach, of the lower urinary tract, and glioblastoma.

Identification of human IKK-2 inhibitors of natural origin (Part II): in Silico prediction of IKK-2 inhibitors in natural extracts with known anti-inflammatory activity.

Human inhibitor NF-κB kinase 2 (hIKK-2) is the primary component responsible for activating NF-κB in response to various inflammatory stimuli. Thus, synthetic ATP-competitive inhibitors for hIKK-2 have been developed as anti-inflammatory compounds. We recently reported a virtual screening protocol (doi:10.1371/journal.pone.0016903) that is able to identify hIKK-2 inhibitors that are not structurally related to any known molecule that inhibits hIKK-2 and that have never been reported to have anti-inflammatory activity. In this study, a stricter version of this protocol was applied to an in-house database of 29,779 natural products annotated with their natural source. The search identified 274 molecules (isolated from 453 different natural extracts) predicted to inhibit hIKK-2. An exhaustive bibliographic search revealed that anti-inflammatory activity has been previously described for: (a) 36 out of these 453 extracts; and (b) 17 out of 30 virtual screening hits present in these 36 extracts. Only one of the remaining 13 hit molecules in these extracts shows chemical similarity with known synthetic hIKK-2 inhibitors. Therefore, it is plausible that a significant portion of the remaining 12 hit molecules are lead-hopping candidates for the development of new hIKK-2 inhibitors.

Official Positions for FRAX® clinical regarding rheumatoid arthritis from Joint Official Positions Development Conference of the International Society for Clinical Densitometry and International Osteoporosis Foundation on FRAX®.

Rheumatoid arthritis is the only secondary cause of osteoporosis that is considered independent of bone density in the FRAX(®) algorithm. Although input for rheumatoid arthritis in FRAX(®) is a dichotomous variable, intuitively, one would expect that more severe or active disease would be associated with a greater risk for fracture. We reviewed the literature to determine if specific disease parameters or medication use could be used to better characterize fracture risk in individuals with rheumatoid arthritis. Although many studies document a correlation between various parameters of disease activity or severity and decreased bone density, fewer have associated these variables with fracture risk. We reviewed these studies in detail and concluded that disability measures such as HAQ (Health Assessment Questionnaire) and functional class do correlate with clinical fractures but not morphometric vertebral fractures. One large study found a strong correlation with duration of disease and fracture risk but additional studies are needed to confirm this. There was little evidence to correlate other measures of disease such as DAS (disease activity score), VAS (visual analogue scale), acute phase reactants, use of non-glucocorticoid medications and increased fracture risk. We concluded that FRAX(®) calculations may underestimate fracture probability in patients with impaired functional status from rheumatoid arthritis but that this could not be quantified at this time. At this time, other disease measures cannot be used for fracture prediction. However only a few, mostly small studies addressed other disease parameters and further research is needed. Additional questions for future research are suggested.

Little evidence that hepatitis C virus leads to a higher risk of mortality in the absence of cirrhosis and excess alcohol intake: the Swiss Hepatitis C Cohort Study.

The objective of this study was to describe the all-cause mortality of participants in the Swiss Hepatitis C Cohort compared to the Swiss general population. Patients with hepatitis C virus (HCV) infection attending secondary and tertiary care centres in Switzerland. One thousand six hundred and forty-five patients with HCV infection were followed up for a mean of over 2 years. We calculated all-cause standardized mortality ratios (SMR) and 95% confidence intervals (CI) using age, sex and calendar year-specific Swiss all-cause mortality rates. Multivariable Poisson regression was used to model the variability of SMR by cirrhotic status, HCV genotype, infection with hepatitis B virus or HIV, injection drug use and alcohol intake. Sixty-one deaths were recorded out of 1645 participants. The crude all-cause SMR was 4.5 (95% CI: 3.5-5.8). Patients co-infected with HIV had a crude SMR of 20 (95% CI: 11.1-36.1). The SMR of 1.1 (95% CI: 0.63-2.03) for patients who were not cirrhotic, not infected with HBV or HIV, did not inject drugs, were not heavy alcohol consumers (<or=40 g/day) and were not genotype 3, indicated no strong evidence of excess mortality. We found little evidence of excess mortality in hepatitis C infected patients who were not cirrhotic, in the absence of selected risk factors. Our findings emphasize the importance of providing appropriate preventive advice, such as counselling to avoid alcohol intake, in those infected with HCV.

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.

Experimental analysis and computer prediction of CTF/NFI transcription factor DNA binding sites.

Accurate prediction of transcription factor binding sites is needed to unravel the function and regulation of genes discovered in genome sequencing projects. To evaluate current computer prediction tools, we have begun a systematic study of the sequence-specific DNA-binding of a transcription factor belonging to the CTF/NFI family. Using a systematic collection of rationally designed oligonucleotides combined with an in vitro DNA binding assay, we found that the sequence specificity of this protein cannot be represented by a simple consensus sequence or weight matrix. For instance, CTF/NFI uses a flexible DNA binding mode that allows for variations of the binding site length. From the experimental data, we derived a novel prediction method using a generalised profile as a binding site predictor. Experimental evaluation of the generalised profile indicated that it accurately predicts the binding affinity of the transcription factor to natural or synthetic DNA sequences. Furthermore, the in vitro measured binding affinities of a subset of oligonucleotides were found to correlate with their transcriptional activities in transfected cells. The combined computational-experimental approach exemplified in this work thus resulted in an accurate prediction method for CTF/NFI binding sites potentially functioning as regulatory regions in vivo.

Longevity differs among sexes but is not affected by repeated immune activation in voles (Microtus arvalis)

Investment of resources in immune defences, despite obvious short-term benefits, may be detrimental to long-term maintenance and thus decrease longevity in absence of parasites. In addition, females and males may differ in immune investment and intrinsic longevity because they are subjected to different degrees of sexual competition and extrinsic mortality. In order to test if sex-specific investment in mounting an immune response reduced longevity, we compared the longevity of captive male and female common voles Microtus arvalis regularly challenged with keyhole limpet haemocyanin, an antigen which elicits the production of antibodies, to the longevity of voles injected with the corresponding antigen-free buffer (phosphate-buffered saline). Injections were repeated every 28 days to mimic a chronic infection. The magnitude of immune response did not vary between males and females and did not affect longevity. Overall, females lived longer than males, independently of the immune challenge. Thus, the long-term costs of immunity seem small in voles. The longevity pattern is consistent with the prediction that male-biased predation or parasitism in the wild causes reduced intrinsic lifespan, but this reduction is not mediated by a decrease in male immunity

Improvement of cardiovascular risk prediction: time to review current knowledge, debates, and fundamentals on how to assess test characteristics.

Cardiovascular risk assessment might be improved with the addition of emerging, new tests derived from atherosclerosis imaging, laboratory tests or functional tests. This article reviews relative risk, odds ratios, receiver-operating curves, posttest risk calculations based on likelihood ratios, the net reclassification improvement and integrated discrimination. This serves to determine whether a new test has an added clinical value on top of conventional risk testing and how this can be verified statistically. Two clinically meaningful examples serve to illustrate novel approaches. This work serves as a review and basic work for the development of new guidelines on cardiovascular risk prediction, taking into account emerging tests, to be proposed by members of the 'Taskforce on Vascular Risk Prediction' under the auspices of the Working Group 'Swiss Atherosclerosis' of the Swiss Society of Cardiology in the future.

Mortality of patients with COPD participating in chronic disease management programmes: a happy end?

BACKGROUND: Concerns about increased mortality could question the role of COPD chronic disease management (CDM) programmes. We aimed at extending a recent Cochrane review to assess the effects of CDM on mortality in patients with COPD. METHODS: Mortality data were available for 25 out of 29 trials identified in a COPD integrated care systematic review. Meta-analysis using random-effects models was performed, followed by subgroup analyses according to study length (3-12 months vs >12 months), main intervention component (exercise, self-management, structured follow-up) and use of an action plan. RESULTS: The meta-analysis showed no impact of CDM on mortality (pooled OR: 1.00, 95% CI 0.79 to 1.28). CONCLUSIONS: These results do not suggest that CDM programmes expose patients with COPD to excessive mortality risk.

Contribution of multimorbidity to overall mortality in the population-based CoLaus study

Introduction : Multimorbidity (MM) is currently a major health concern for hospitalized patients but little is known about the relative importance of MM in the general population. Accordingly we assessed whether MM could be a good predictor of overall mortality. Method : Data from the population based CoLaus Study: 3239 participants (1731 women, mean age 50+/-9 years) followed for a median time of 5.4 years (range 0.4 to 8.5 years). MM was defined as presenting >=2 morbidities according to Barnett et al. (27 items, measured data). Survival analysis was conducted using Cox regression. Results : During follow-up, 53 (1.6%) participants died. Participants who died had a higher number of morbidities (2.4 +/- 1.6 vs. 1.9 +/- 1.5, p<0.05) and had a higher prevalence of MM (69.8% vs. 55.9%, p<0.05). On bivariate analysis, presence of MM (defined as a yes/no variable) was significantly related with overall mortality: relative risk (RR) of 1.84, 95% confidence interval [1.02; 3.31], p<0.05 (see figure), but this association became non-significant after adjusting for age, gender and smoking: RR=1.68 [0.93; 3.04], p=0.09. Similar results were obtained when using the number of morbidities: RR for an extra morbidity 1.22 [1.05; 1.44], p<0.02; after adjusting for age, gender and smoking, RR=1.16 [0.99; 1.37], p=0.07. Conclusion : During a short 5 year observation period, measured MM in the general population is associated with overall mortality. This association becomes borderline significant after multivariate adjustment. These observations will have to be confirmed during a longer follow-up period. This increased mortality in MM patients may require developing specific strategies of screening and prevention.

The circadian rhythm of the perinatal mortality rate in Switzerland.

The authors examine the relation between the perinatal mortality rate (PMR), birth weight in four categories, and hour of birth throughout the week in Switzerland, using data on 672,013 births and 5,764 perinatal deaths recorded between 1979 and 1987. From Monday to Friday, the PMR follows a circadian rhythm with a regular increase from early morning to evening, with a peak for babies born between 7 and 8 p.m. This pattern of variation has two main components: The circadian rhythms for the proportion of births in the four weight categories and the PMR circadian rhythm for babies weighing more than 2.5 kg. According to a cosinor model, which describes about 40% of the total variation in the PMR, the most important determinants are changes in the proportions of births: Low birth weight increases toward the afternoon and night. Mechanisms underlying the weight-specific timing of birth are discussed, including time selection of birth according to obstetric risks, the direct effect of neonatal and obstetric care, and chronobiologic behavior.