Analysis and spatial modeling of the indoor radon Swiss data

The present research deals with an important public health threat, which is the pollution created by radon gas accumulation inside dwellings. The spatial modeling of indoor radon in Switzerland is particularly complex and challenging because of many influencing factors that should be taken into account. Indoor radon data analysis must be addressed from both a statistical and a spatial point of view. As a multivariate process, it was important at first to define the influence of each factor. In particular, it was important to define the influence of geology as being closely associated to indoor radon. This association was indeed observed for the Swiss data but not probed to be the sole determinant for the spatial modeling. The statistical analysis of data, both at univariate and multivariate level, was followed by an exploratory spatial analysis. Many tools proposed in the literature were tested and adapted, including fractality, declustering and moving windows methods. The use of Quan-tité Morisita Index (QMI) as a procedure to evaluate data clustering in function of the radon level was proposed. The existing methods of declustering were revised and applied in an attempt to approach the global histogram parameters. The exploratory phase comes along with the definition of multiple scales of interest for indoor radon mapping in Switzerland. The analysis was done with a top-to-down resolution approach, from regional to local lev¬els in order to find the appropriate scales for modeling. In this sense, data partition was optimized in order to cope with stationary conditions of geostatistical models. Common methods of spatial modeling such as Κ Nearest Neighbors (KNN), variography and General Regression Neural Networks (GRNN) were proposed as exploratory tools. In the following section, different spatial interpolation methods were applied for a par-ticular dataset. A bottom to top method complexity approach was adopted and the results were analyzed together in order to find common definitions of continuity and neighborhood parameters. Additionally, a data filter based on cross-validation was tested with the purpose of reducing noise at local scale (the CVMF). At the end of the chapter, a series of test for data consistency and methods robustness were performed. This lead to conclude about the importance of data splitting and the limitation of generalization methods for reproducing statistical distributions. The last section was dedicated to modeling methods with probabilistic interpretations. Data transformation and simulations thus allowed the use of multigaussian models and helped take the indoor radon pollution data uncertainty into consideration. The catego-rization transform was presented as a solution for extreme values modeling through clas-sification. Simulation scenarios were proposed, including an alternative proposal for the reproduction of the global histogram based on the sampling domain. The sequential Gaussian simulation (SGS) was presented as the method giving the most complete information, while classification performed in a more robust way. An error measure was defined in relation to the decision function for data classification hardening. Within the classification methods, probabilistic neural networks (PNN) show to be better adapted for modeling of high threshold categorization and for automation. Support vector machines (SVM) on the contrary performed well under balanced category conditions. In general, it was concluded that a particular prediction or estimation method is not better under all conditions of scale and neighborhood definitions. Simulations should be the basis, while other methods can provide complementary information to accomplish an efficient indoor radon decision making.

Very high-resolution environmental predictors in species distribution models: moving beyond topography?

Recent advances in remote sensing technologies have facilitated the generation of very high resolution (VHR) environmental data. Exploratory studies suggested that, if used in species distribution models (SDMs), these data should enable modelling species' micro-habitats and allow improving predictions for fine-scale biodiversity management. In the present study, we tested the influence, in SDMs, of predictors derived from a VHR digital elevation model (DEM) by comparing the predictive power of models for 239 plant species and their assemblages fitted at six different resolutions in the Swiss Alps. We also tested whether changes of the model quality for a species is related to its functional and ecological characteristics. Refining the resolution only contributed to slight improvement of the models for more than half of the examined species, with the best results obtained at 5 m, but no significant improvement was observed, on average, across all species. Contrary to our expectations, we could not consistently correlate the changes in model performance with species characteristics such as vegetation height. Temperature, the most important variable in the SDMs across the different resolutions, did not contribute any substantial improvement. Our results suggest that improving resolution of topographic data only is not sufficient to improve SDM predictions - and therefore local management - compared to previously used resolutions (here 25 and 100 m). More effort should be dedicated now to conduct finer-scale in-situ environmental measurements (e.g. for temperature, moisture, snow) to obtain improved environmental measurements for fine-scale species mapping and management.

Free-breathing 3D coronary MRA: the impact of "isotropic" image resolution.

During conventional x-ray coronary angiography, multiple projections of the coronary arteries are acquired to define coronary anatomy precisely. Due to time constraints, coronary magnetic resonance angiography (MRA) usually provides only one or two views of the major coronary vessels. A coronary MRA approach that allowed for reconstruction of arbitrary isotropic orientations might therefore be desirable. The purpose of the study was to develop a three-dimensional (3D) coronary MRA technique with isotropic image resolution in a relatively short scanning time that allows for reconstruction of arbitrary views of the coronary arteries without constraints given by anisotropic voxel size. Eight healthy adult subjects were examined using a real-time navigator-gated and corrected free-breathing interleaved echoplanar (TFE-EPI) 3D-MRA sequence. Two 3D datasets were acquired for the left and right coronary systems in each subject, one with anisotropic (1.0 x 1.5 x 3.0 mm, 10 slices) and one with "near" isotropic (1.0 x 1.5 x 1.0 mm, 30 slices) image resolution. All other imaging parameters were maintained. In all cases, the entire left main (LM) and extensive portions of the left anterior descending (LAD) and the right coronary artery (RCA) were visualized. Objective assessment of coronary vessel sharpness was similar (41% +/- 5% vs. 42% +/- 5%; P = NS) between in-plane and through-plane views with "isotropic" voxel size but differed (32% +/- 7% vs. 23% +/- 4%; P < 0.001) with nonisotropic voxel size. In reconstructed views oriented in the through-plane direction, the vessel border was 86% more defined (P < 0.01) for isotropic compared with anisotropic images. A smaller (30%; P < 0.001) improvement was seen for in-plane reconstructions. Vessel diameter measurements were view independent (2.81 +/- 0.45 mm vs. 2.66 +/- 0.52 mm; P = NS) for isotropic, but differed (2.71 +/- 0.51 mm vs. 3.30 +/- 0.38 mm; P < 0.001) between anisotropic views. Average scanning time was 2:31 +/- 0:57 minutes for anisotropic and 7:11 +/- 3:02 minutes for isotropic image resolution (P < 0.001). We present a new approach for "near" isotropic 3D coronary artery imaging, which allows for reconstruction of arbitrary views of the coronary arteries. The good delineation of the coronary arteries in all views suggests that isotropic 3D coronary MRA might be a preferred technique for the assessment of coronary disease, although at the expense of prolonged scan times. Comparative studies with conventional x-ray angiography are needed to investigate the clinical utility of the isotropic strategy.

High-resolution three-dimensional aortic magnetic resonance angiography and quantitative vessel wall characterization of different atherosclerotic stages in a rabbit model

PURPOSE: Atherosclerosis results in a considerable medical and socioeconomic impact on society. We sought to evaluate novel magnetic resonance imaging (MRI) angiography and vessel wall sequences to visualize and quantify different morphologic stages of atherosclerosis in a Watanabe hereditary hyperlipidemic (WHHL) rabbit model. MATERIAL AND METHODS: Aortic 3D steady-state free precession angiography and subrenal aortic 3D black-blood fast spin-echo vessel wall imaging pre- and post-Gadolinium (Gd) was performed in 14 WHHL rabbits (3 normal, 6 high-cholesterol diet, and 5 high-cholesterol diet plus endothelial denudation) on a commercial 1.5 T MR system. Angiographic lumen diameter, vessel wall thickness, signal-/contrast-to-noise analysis, total vessel area, lumen area, and vessel wall area were analyzed semiautomatically. RESULTS: Pre-Gd, both lumen and wall dimensions (total vessel area, lumen area, vessel wall area) of group 2 + 3 were significantly increased when compared with those of group 1 (all P < 0.01). Group 3 animals had significantly thicker vessel walls than groups 1 and 2 (P < 0.01), whereas angiographic lumen diameter was comparable among all groups. Post-Gd, only diseased animals of groups 2 + 3 showed a significant (>100%) signal-to-noise ratio and contrast-to-noise increase. CONCLUSIONS: A combination of novel 3D magnetic resonance angiography and high-resolution 3D vessel wall MRI enabled quantitative characterization of various atherosclerotic stages including positive arterial remodeling and Gd uptake in a WHHL rabbit model using a commercially available 1.5 T MRI system.

High-resolution ammonite and carbon isotope stratigraphy across the Triassic-Jurassic boundary at New York Canyon (Nevada)

The Triassic-Jurassic boundary is generally considered as one of the major extinctions in the history of Phanerozoic. The high-resolution ammonite correlations and carbon isotope marine record in the New York Canyon area allow to distinguish two negative carbon excursions across this boundary with different paleoenvironmental meanings. The Late Rhaetian negative excursion is related to the extinction and regressive phase. The Early Hettangian delta(13)C(org) negative excursion is associated with a major floristic turnover and major ammonite and radiolarian radiation. The end-Triassic extinction-Early Jurassic recovery is fully compatible with a volcanism-triggered crisis, probably related to the Central Atlantic Magmatic Province. The main environmental stress might have been generated by repeated release of SO(2) gas, heavy metals emissions, darkening, and subsequent cooling. This phase was followed by a major long-term CO(2) accumulation during the Early Hettangian with development of nutrient-rich marine waters favouring the recovery of productivity and deposition of black shales. (C) 2004 Elsevier B.V. All rights reserved.

Iron-deficiency anemia and hematochezia: when is colonoscopy appropriate?

Background: Colonoscopy is usually proposed for the evaluation of lower gastrointestinal blood loss (hematochezia) or iron deficiency anemia (IDA). Clinical practice guidelines support this approach but formal evidence is lacking. Real clinical scenarios made available on the web would be of great help in decision-making in clinical practice as to whether colonoscopy is appropriate for a given patient. Method: A multidisciplinary multinational expert panel (EPAGE II) developed appropriateness criteria based on best published evidence (systematic reviews, clinical trials, guidelines) and experts' judgement. Using the explicit RAND Appropriateness Method (3 round of experts' votes and a panel meeting) 102 clinical scenarios were judged inappropriate, uncertain, appropriate, or necessary. Results: In IDA, colonoscopy was appropriate in patients >50 years and necessary in the presence of lower abdominal symptoms. In both men and women aged <50 years, colonoscopy was appropriate if prior sigmoidoscopy and/or gastroscopy did not explain the IDA, and necessary if lower gastrointestinal symptoms were present. In women <50 years with a potential gynecological cause, additional lower gastrointestinal symptoms rendered colonoscopy appropriate. In patients >50 years with hematochezia, colonoscopy was always appropriate and mostly necessary, except if a prior colonoscopy was normal within the previous 5 years. Under age 50 years, the presence of any risk factor for colorectal cancer (CRC) and no previous normal colonoscopy (within the last 5 years) made this procedure appropriate and necessary. Conclusion: Colonoscopy is appropriate and even necessary for many indications related to iron deficiency anemia or hematochezia, in particular in patients aged >50 years. The main factors influencing appropriateness are age, results of prior investigations (sigmoidoscopy, gastroscopy, previous colonoscopy), CRC risk and sex. EPAGE II appropriateness criteria are available on www.epage.ch

Inversion of multiple intersecting high-resolution crosshole GPR profiles for hydrological characterization at the Boise Hydrogeophysical Research Site

The integration of geophysical data into the subsurface characterization problem has been shown in many cases to significantly improve hydrological knowledge by providing information at spatial scales and locations that is unattainable using conventional hydrological measurement techniques. In particular, crosshole ground-penetrating radar (GPR) tomography has shown much promise in hydrology because of its ability to provide highly detailed images of subsurface radar wave velocity, which is strongly linked to soil water content. Here, we develop and demonstrate a procedure for inverting together multiple crosshole GPR data sets in order to characterize the spatial distribution of radar wave velocity below the water table at the Boise Hydrogeophysical Research Site (BHRS) near Boise, Idaho, USA. Specifically, we jointly invert 31 intersecting crosshole GPR profiles to obtain a highly resolved and consistent radar velocity model along the various profile directions. The model is found to be strongly correlated with complementary neutron porosity-log data and is further corroborated by larger-scale structural information at the BHRS. This work is an important prerequisite to using crosshole GPR data together with existing hydrological measurements for improved groundwater flow and contaminant transport modeling.

Investigation of high-resolution functional magnetic resonance imaging by means of surface and array radiofrequency coils at 7 T.

In this investigation, high-resolution, 1x1x1-mm(3) functional magnetic resonance imaging (fMRI) at 7 T is performed using a multichannel array head coil and a surface coil approach. Scan geometry was optimized for each coil separately to exploit the strengths of both coils. Acquisitions with the surface coil focused on partial brain coverage, while whole-brain coverage fMRI experiments were performed with the array head coil. BOLD sensitivity in the occipital lobe was found to be higher with the surface coil than with the head array, suggesting that restriction of signal detection to the area of interest may be beneficial for localized activation studies. Performing independent component analysis (ICA) decomposition of the fMRI data, we consistently detected BOLD signal changes and resting state networks. In the surface coil data, a small negative BOLD response could be detected in these resting state network areas. Also in the data acquired with the surface coil, two distinct components of the positive BOLD signal were consistently observed. These two components were tentatively assigned to tissue and venous signal changes.

Kernel-based Mapping of Meteorological Fields in Complex Orography

Due to the advances in sensor networks and remote sensing technologies, the acquisition and storage rates of meteorological and climatological data increases every day and ask for novel and efficient processing algorithms. A fundamental problem of data analysis and modeling is the spatial prediction of meteorological variables in complex orography, which serves among others to extended climatological analyses, for the assimilation of data into numerical weather prediction models, for preparing inputs to hydrological models and for real time monitoring and short-term forecasting of weather.In this thesis, a new framework for spatial estimation is proposed by taking advantage of a class of algorithms emerging from the statistical learning theory. Nonparametric kernel-based methods for nonlinear data classification, regression and target detection, known as support vector machines (SVM), are adapted for mapping of meteorological variables in complex orography.With the advent of high resolution digital elevation models, the field of spatial prediction met new horizons. In fact, by exploiting image processing tools along with physical heuristics, an incredible number of terrain features which account for the topographic conditions at multiple spatial scales can be extracted. Such features are highly relevant for the mapping of meteorological variables because they control a considerable part of the spatial variability of meteorological fields in the complex Alpine orography. For instance, patterns of orographic rainfall, wind speed and cold air pools are known to be correlated with particular terrain forms, e.g. convex/concave surfaces and upwind sides of mountain slopes.Kernel-based methods are employed to learn the nonlinear statistical dependence which links the multidimensional space of geographical and topographic explanatory variables to the variable of interest, that is the wind speed as measured at the weather stations or the occurrence of orographic rainfall patterns as extracted from sequences of radar images. Compared to low dimensional models integrating only the geographical coordinates, the proposed framework opens a way to regionalize meteorological variables which are multidimensional in nature and rarely show spatial auto-correlation in the original space making the use of classical geostatistics tangled.The challenges which are explored during the thesis are manifolds. First, the complexity of models is optimized to impose appropriate smoothness properties and reduce the impact of noisy measurements. Secondly, a multiple kernel extension of SVM is considered to select the multiscale features which explain most of the spatial variability of wind speed. Then, SVM target detection methods are implemented to describe the orographic conditions which cause persistent and stationary rainfall patterns. Finally, the optimal splitting of the data is studied to estimate realistic performances and confidence intervals characterizing the uncertainty of predictions.The resulting maps of average wind speeds find applications within renewable resources assessment and opens a route to decrease the temporal scale of analysis to meet hydrological requirements. Furthermore, the maps depicting the susceptibility to orographic rainfall enhancement can be used to improve current radar-based quantitative precipitation estimation and forecasting systems and to generate stochastic ensembles of precipitation fields conditioned upon the orography.

Pseudomonas aeruginosa serotypes in nosocomial pneumonia: prevalence and clinical outcomes.

INTRODUCTION: Pseudomonas aeruginosa frequently causes nosocomial pneumonia and is associated with poor outcome. The purpose of this study was to assess the prevalence and clinical outcome of nosocomial pneumonia caused by serotype-specific P. aeruginosa in critically ill patients under appropriate antimicrobial therapy management. METHODS: A retrospective, non-interventional epidemiological multicenter cohort study involving 143 patients with confirmed nosocomial pneumonia caused by P. aeruginosa. Patients were analyzed for a period of 30 days from time of nosocomial pneumonia onset. Fourteen patients fulfilling the same criteria from a phase IIa studyconducted at the same time/centers were included in the prevalence calculations but not in the clinical outcome analysis. RESULTS: The prevalence of serotypes was: O6 (29%), O11 (23%), O10 (10%), O2 (9%), and O1 (8%). Serotypes with a prevalence of less than 5% were found in 13% of patients, 8% were classified as not typeable. Across all serotypes, 19% mortality, 70% clinical resolution, 11% clinical continuation, and 5% clinical recurrence were recorded. Age and higher APACHE II (Acute Physiology and Chronic Health Evaluation II) were predictive risk factors associated with probability of death and lower clinical resolution for P. aeruginosa nosocomial pneumonia. Mortality tends to be higher with O1 (40%) and lower with O2 (0%); clinical resolution tends to be better with O2 (82%) compared to other serotypes. Persisting pneumonia with O6 and O11 was, respectively, 8% and 21%; clinical resolution with O6 and O11 was, respectively, 75% and 57%. CONCLUSIONS: In P. aeruginosa nosocomial pneumonia, the most prevalent serotypes were O6 and O11. Further studies including larger group sizes are needed to correlate clinical outcome with virulence factors of P. aeruginosa in patients with nosocomial pneumonia caused by various serotypes; and to compare O6 and O11, the two serotypes most frequently encountered in critically ill patients.

Sublimation of new matrix candidates for high spatial resolution imaging mass spectrometry of lipids: enhanced information in both positive and negative polarities after 1,5-diaminonapthalene deposition.

Matrix sublimation has demonstrated to be a powerful approach for high-resolution matrix-assisted laser desorption ionization (MALDI) imaging of lipids, providing very homogeneous solvent-free deposition. This work presents a comprehensive study aiming to evaluate current and novel matrix candidates for high spatial resolution MALDI imaging mass spectrometry of lipids from tissue section after deposition by sublimation. For this purpose, 12 matrices including 2,5-dihydroxybenzoic acid (DHB), sinapinic acid (SA), α-cyano-4-hydroxycinnamic acid (CHCA), 2,6-dihydroxyacetphenone (DHA), 2',4',6'-trihydroxyacetophenone (THAP), 3-hydroxypicolinic acid (3-HPA), 1,8-bis(dimethylamino)naphthalene (DMAN), 1,8,9-anthracentriol (DIT), 1,5-diaminonapthalene (DAN), p-nitroaniline (NIT), 9-aminoacridine (9-AA), and 2-mercaptobenzothiazole (MBT) were investigated for lipid detection efficiency in both positive and negative ionization modes, matrix interferences, and stability under vacuum. For the most relevant matrices, ion maps of the different lipid species were obtained from tissue sections at high spatial resolution and the detected peaks were characterized by matrix-assisted laser desorption ionization time-of-flight/time-of-flight (MALDI-TOF/TOF) mass spectrometry. First proposed for imaging mass spectrometry (IMS) after sublimation, DAN has demonstrated to be of high efficiency providing rich lipid signatures in both positive and negative polarities with high vacuum stability and sub-20 μm resolution capacity. Ion images from adult mouse brain were generated with a 10 μm scanning resolution. Furthermore, ion images from adult mouse brain and whole-body fish tissue sections were also acquired in both polarity modes from the same tissue section at 100 μm spatial resolution. Sublimation of DAN represents an interesting approach to improve information with respect to currently employed matrices providing a deeper analysis of the lipidome by IMS.

Is it appropriate to index glomerular filtration rate for body surface area

Purpose: Obesity is an established independent risk factor for chronic kidney disease. Thus, measurement of glomerular filtration rate (GFR) is important in this population. Traditionally, GFR has been indexed for body surface area (BSA), but this indexation may not be appropriate in obese individuals. Therefore, the objective of the study was to compare absolute GFR with GFR indexed for BSA and with GFR indexed for height. Methods and materials: The study was conducted in 66 families from the Seychelles islands that included several members with hypertension. GFR and effective renal plasma flow (ERPF) were measured using inulin and PAH clearances, respectively. Antihypertensive treatment, if used, was withheld 2 weeks before conducting the clearances. Participants with diabetes mellitus were excluded from the analysis. BSA was calculated using the Dubois formula. We assessed trend across BMI categories using a non parametric test. Results: Participants included 174 women and 127 men. The prevalence of hypertension was 61%, of which 68% were treated. The table shows that absolute GFR, GFR indexed for height, ERPF, filtration fraction were significantly higher across BMI categories. When GFR was indexed for BSA, the association between GFR and BMI categories was lost. Conclusion: Indexing GFR for BSA in overweight and obese individuals leads to a substantial underestimation of GFR. Filtration fraction, which does not depend on BSA, is higher in obese individuals, which suggests glomerular hyperfiltration. Indexing GFR for BSA therefore would mask the underlying glomerular hyperfiltration. As the number of nephrons does not increase with weight gain, absolute GFR represents a better marker of single nephron GFR and is more appropriate.

Quantitative integration of high-resolution hydrogeophysical data through Monte-Carlo-type conditional simulations

Geophysical techniques can help to bridge the inherent gap with regard to spatial resolution and the range of coverage that plagues classical hydrological methods. This has lead to the emergence of the new and rapidly growing field of hydrogeophysics. Given the differing sensitivities of various geophysical techniques to hydrologically relevant parameters and their inherent trade-off between resolution and range the fundamental usefulness of multi-method hydrogeophysical surveys for reducing uncertainties in data analysis and interpretation is widely accepted. A major challenge arising from such endeavors is the quantitative integration of the resulting vast and diverse database in order to obtain a unified model of the probed subsurface region that is internally consistent with all available data. To address this problem, we have developed a strategy towards hydrogeophysical data integration based on Monte-Carlo-type conditional stochastic simulation that we consider to be particularly suitable for local-scale studies characterized by high-resolution and high-quality datasets. Monte-Carlo-based optimization techniques are flexible and versatile, allow for accounting for a wide variety of data and constraints of differing resolution and hardness and thus have the potential of providing, in a geostatistical sense, highly detailed and realistic models of the pertinent target parameter distributions. Compared to more conventional approaches of this kind, our approach provides significant advancements in the way that the larger-scale deterministic information resolved by the hydrogeophysical data can be accounted for, which represents an inherently problematic, and as of yet unresolved, aspect of Monte-Carlo-type conditional simulation techniques. We present the results of applying our algorithm to the integration of porosity log and tomographic crosshole georadar data to generate stochastic realizations of the local-scale porosity structure. Our procedure is first tested on pertinent synthetic data and then applied to corresponding field data collected at the Boise Hydrogeophysical Research Site near Boise, Idaho, USA.