Multilayer perceptron with local constraint as an emerging method in spatial data analysis

The use of Geographic Information Systems has revolutionalized the handling and the visualization of geo-referenced data and has underlined the critic role of spatial analysis. The usual tools for such a purpose are geostatistics which are widely used in Earth science. Geostatistics are based upon several hypothesis which are not always verified in practice. On the other hand, Artificial Neural Network (ANN) a priori can be used without special assumptions and are known to be flexible. This paper proposes to discuss the application of ANN in the case of the interpolation of a geo-referenced variable.

On the Schoenberg transformations in data analysis: theory and illustrations

The class of Schoenberg transformations, embedding Euclidean distances into higher dimensional Euclidean spaces, is presented, and derived from theorems on positive definite and conditionally negative definite matrices. Original results on the arc lengths, angles and curvature of the transformations are proposed, and visualized on artificial data sets by classical multidimensional scaling. A distance-based discriminant algorithm and a robust multidimensional centroid estimate illustrate the theory, closely connected to the Gaussian kernels of Machine Learning.

How to interprete subclinical thyroid dysfunction in the prevention and management of heart failure events? Individual participant data analysis from six prospective cohorts

Background: Guidelines of the Diagnosis and Management of Heart Failure (HF) recommend investigating exacerbating conditions, such as thyroid dysfunction, but without specifying impact of different TSH levels. Limited prospective data exist regarding the association between subclinical thyroid dysfunction and HF events. Methods: We performed a pooled analysis of individual participant data using all available prospective cohorts with thyroid function tests and subsequent follow-up of HF events. Individual data on 25,390 participants with 216,247 person-years of follow-up were supplied from 6 prospective cohorts in the United States and Europe. Euthyroidism was defined as TSH 0.45-4.49 mIU/L, subclinical hypothyroidism as TSH 4.5-19.9 mIU/L and subclinical hyperthyroidism as TSH <0.45 mIU/L, both with normal free thyroxine levels. HF events were defined as acute HF events, hospitalization or death related to HF events. Results: Among 25,390 participants, 2068 had subclinical hypothyroidism (8.1%) and 648 subclinical hyperthyroidism (2.6%). In age- and gender-adjusted analyses, risks of HF events were increased with both higher and lower TSH levels (P for quadratic pattern<0.01): hazard ratio (HR) was 1.01 (95% confidence interval [CI] 0.81-1.26) for TSH 4.5-6.9 mIU/L, 1.65 (CI 0.84-3.23) for TSH 7.0-9.9 mIU/L, 1.86 (CI 1.27-2.72) for TSH 10.0-19.9 mIUL/L (P for trend <0.01), and was 1.31 (CI 0.88-1.95) for TSH 0.10-0.44 mIU/L and 1.94 (CI 1.01-3.72) for TSH <0.10 mIU/L (P for trend=0.047). Risks remained similar after adjustment for cardiovascular risk factors. Conclusion: Risks of HF events were increased with both higher and lower TSH levels, particularly for TSH ≥10 mIU/L and for TSH <0.10 mIU/L. Our findings might help to interpret TSH levels in the prevention and investigation of HF.

Subclinical thyroid dysfunction and the risk of heart failure events: an individual participant data analysis from 6 prospective cohorts.

BACKGROUND: American College of Cardiology/American Heart Association guidelines for the diagnosis and management of heart failure recommend investigating exacerbating conditions such as thyroid dysfunction, but without specifying the impact of different thyroid-stimulation hormone (TSH) levels. Limited prospective data exist on the association between subclinical thyroid dysfunction and heart failure events. METHODS AND RESULTS: We performed a pooled analysis of individual participant data using all available prospective cohorts with thyroid function tests and subsequent follow-up of heart failure events. Individual data on 25 390 participants with 216 248 person-years of follow-up were supplied from 6 prospective cohorts in the United States and Europe. Euthyroidism was defined as TSH of 0.45 to 4.49 mIU/L, subclinical hypothyroidism as TSH of 4.5 to 19.9 mIU/L, and subclinical hyperthyroidism as TSH <0.45 mIU/L, the last two with normal free thyroxine levels. Among 25 390 participants, 2068 (8.1%) had subclinical hypothyroidism and 648 (2.6%) had subclinical hyperthyroidism. In age- and sex-adjusted analyses, risks of heart failure events were increased with both higher and lower TSH levels (P for quadratic pattern <0.01); the hazard ratio was 1.01 (95% confidence interval, 0.81-1.26) for TSH of 4.5 to 6.9 mIU/L, 1.65 (95% confidence interval, 0.84-3.23) for TSH of 7.0 to 9.9 mIU/L, 1.86 (95% confidence interval, 1.27-2.72) for TSH of 10.0 to 19.9 mIU/L (P for trend <0.01) and 1.31 (95% confidence interval, 0.88-1.95) for TSH of 0.10 to 0.44 mIU/L and 1.94 (95% confidence interval, 1.01-3.72) for TSH <0.10 mIU/L (P for trend=0.047). Risks remained similar after adjustment for cardiovascular risk factors. CONCLUSION: Risks of heart failure events were increased with both higher and lower TSH levels, particularly for TSH ≥10 and <0.10 mIU/L.

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.

Thyroid antibody status, subclinical hypothyroidism, and the risk of coronary heart disease: an individual participant data analysis.

CONTEXT: Subclinical hypothyroidism has been associated with increased risk of coronary heart disease (CHD), particularly with thyrotropin levels of 10.0 mIU/L or greater. The measurement of thyroid antibodies helps predict the progression to overt hypothyroidism, but it is unclear whether thyroid autoimmunity independently affects CHD risk. OBJECTIVE: The objective of the study was to compare the CHD risk of subclinical hypothyroidism with and without thyroid peroxidase antibodies (TPOAbs). DATA SOURCES AND STUDY SELECTION: A MEDLINE and EMBASE search from 1950 to 2011 was conducted for prospective cohorts, reporting baseline thyroid function, antibodies, and CHD outcomes. DATA EXTRACTION: Individual data of 38 274 participants from six cohorts for CHD mortality followed up for 460 333 person-years and 33 394 participants from four cohorts for CHD events. DATA SYNTHESIS: Among 38 274 adults (median age 55 y, 63% women), 1691 (4.4%) had subclinical hypothyroidism, of whom 775 (45.8%) had positive TPOAbs. During follow-up, 1436 participants died of CHD and 3285 had CHD events. Compared with euthyroid individuals, age- and gender-adjusted risks of CHD mortality in subclinical hypothyroidism were similar among individuals with and without TPOAbs [hazard ratio (HR) 1.15, 95% confidence interval (CI) 0.87-1.53 vs HR 1.26, CI 1.01-1.58, P for interaction = .62], as were risks of CHD events (HR 1.16, CI 0.87-1.56 vs HR 1.26, CI 1.02-1.56, P for interaction = .65). Risks of CHD mortality and events increased with higher thyrotropin, but within each stratum, risks did not differ by TPOAb status. CONCLUSIONS: CHD risk associated with subclinical hypothyroidism did not differ by TPOAb status, suggesting that biomarkers of thyroid autoimmunity do not add independent prognostic information for CHD outcomes.

Subclinical Hypothyroidism and the Risk of Stroke Events and Fatal Stroke: An Individual Participant Data Analysis.

OBJECTIVE: The objective was to determine the risk of stroke associated with subclinical hypothyroidism. DATA SOURCES AND STUDY SELECTION: Published prospective cohort studies were identified through a systematic search through November 2013 without restrictions in several databases. Unpublished studies were identified through the Thyroid Studies Collaboration. We collected individual participant data on thyroid function and stroke outcome. Euthyroidism was defined as TSH levels of 0.45-4.49 mIU/L, and subclinical hypothyroidism was defined as TSH levels of 4.5-19.9 mIU/L with normal T4 levels. DATA EXTRACTION AND SYNTHESIS: We collected individual participant data on 47 573 adults (3451 subclinical hypothyroidism) from 17 cohorts and followed up from 1972-2014 (489 192 person-years). Age- and sex-adjusted pooled hazard ratios (HRs) for participants with subclinical hypothyroidism compared to euthyroidism were 1.05 (95% confidence interval [CI], 0.91-1.21) for stroke events (combined fatal and nonfatal stroke) and 1.07 (95% CI, 0.80-1.42) for fatal stroke. Stratified by age, the HR for stroke events was 3.32 (95% CI, 1.25-8.80) for individuals aged 18-49 years. There was an increased risk of fatal stroke in the age groups 18-49 and 50-64 years, with a HR of 4.22 (95% CI, 1.08-16.55) and 2.86 (95% CI, 1.31-6.26), respectively (p trend 0.04). We found no increased risk for those 65-79 years old (HR, 1.00; 95% CI, 0.86-1.18) or ≥ 80 years old (HR, 1.31; 95% CI, 0.79-2.18). There was a pattern of increased risk of fatal stroke with higher TSH concentrations. CONCLUSIONS: Although no overall effect of subclinical hypothyroidism on stroke could be demonstrated, an increased risk in subjects younger than 65 years and those with higher TSH concentrations was observed.

Subclinical hypothyroidism and the risk of coronary heart disease and mortality: an individual participant data analysis from nine prospective cohort studies

Texte intégral: http://www.springerlink.com/content/3q68180337551r47/fulltext.pdf

Role of disease activity for decision making ability in early multiple sclerosis

Background and purpose: Decision making (DM) has been defined as the process through which a person forms preferences, selects and executes actions, and evaluates the outcome related to a selected choice. This ability represents an important factor for adequate behaviour in everyday life. DM impairment in multiple sclerosis (MS) has been previously reported. The purpose of the present study was to assess DM in patients with MS at the earliest clinically detectable time point of the disease. Methods: Patients with definite (n=109) or possible (clinically isolated syndrome, CIS; n=56) MS, a short disease duration (mean 2.3 years) and a minor neurological disability (mean EDSS 1.8) were compared to 50 healthy controls aged 18 to 60 years (mean age 32.2) using the Iowa Gambling Task (IGT). Subjects had to select a card from any of 4 decks (A/B [disadvantageous]; C/D [advantageous]). The game consisted of 100 trials then grouped in blocks of 20 cards for data analysis. Skill in DM was assessed by means of a learning index (LI) defined as the difference between the averaged last three block indexes and first two block indexes (LI=[(BI-3+BI-4+BI-5)/3-(BI-1+B2)/2]). Non parametric tests were used for statistical analysis. Results: LI was higher in the control group (0.24, SD 0.44) than in the MS group (0.21, SD 0.38), however without reaching statistical significance (p=0.7). Interesting differences were detected when MS patients were grouped according to phenotype. A trend to a difference between MS subgroups and controls was observed for LI (p=0.06), which became significant between MS subgroups (p=0.03). CIS patients who confirmed MS diagnosis by presenting a second relapse after study entry showed a dysfunction in the IGT in comparison to the other CIS (p=0.01) and definite MS (p=0.04) patients. In the opposite, CIS patients characterised by not entirely fulfilled McDonald criteria at inclusion and absence of relapse during the study showed an normal learning pattern on the IGT. Finally, comparing MS patients who developed relapses after study entry, those who remained clinically stable and controls, we observed impaired performances only in relapsing patients in comparison to stable patients (p=0.008) and controls (p=0.03). Discussion: These results raise the assumption of a sustained role for both MS relapsing activity and disease heterogeneity (i.e. infra-clinical severity or activity of MS) in the impaired process of decision making.

Chirality in the new generation of antidepressants: stereoselective analysis of the enantiomers of mirtazapine, N-demethylmirtazapine, and 8-hydroxymirtazapine by LC-MS.

Mirtazapine is an antidepressant that acts specifically on noradrenergic and sertonergic receptors. A LC-MS method was developed that allows the simultaneous analysis of the R-(-)- and S-(+)-enantiomers of mirtazapine (MIR), demethylmirtazapine (DMIR), and 8-hydroxymirtazapine (8-OH-MIR) in plasma of MIR-treated patients. The method involves a 3-step liquid-liquid extraction, an HPLC separation on a Chirobiotic V column, and MS detection in electrospray mode. The limit of quantification (LOQ) for all enantiomers was 0.5 ng/mL, and the intra- and interday CVs were within 3.3% to 11.7% (concentration ranges 5-50 ng/mL). A method is also presented for the quantitative analysis of glucuroconjugated MIR and 8-OH-MIR. S-(+)-8-OH-MIR is present in plasma mainly as its glucuronide. Preliminary data suggest that in all patients, except in those comedicated with CYP2D6 inhibitors such as fluoxetine and thioridazine, R-(-)-MIR concentrations were higher than those of S-(+)MIR. Moreover, fluvoxamine seems also to inhibit the metabolism of MIR. Therefore, this method seems to be suitable for the stereoselective assay of MIR and its metabolites in plasma of patients comedicated with MIR and other drugs for routine and research purposes.