Regional-scale integration of multi-scale hydrological and geophysical data using a two-step Bayesian sequential simulation approach

Significant progress has been made with regard to the quantitative integration of geophysical and hydrological data at the local scale for the purpose of improving predictions of groundwater flow and solute transport. However, extending corresponding approaches to the regional scale still represents one of the major challenges in the domain of hydrogeophysics. To address this problem, we have developed a regional-scale data integration methodology based on a two-step Bayesian sequential simulation approach. Our objective is to generate high-resolution stochastic realizations of the regional-scale hydraulic conductivity field in the common case where there exist spatially exhaustive but poorly resolved measurements of a related geophysical parameter, as well as highly resolved but spatially sparse collocated measurements of this geophysical parameter and the hydraulic conductivity. To integrate this multi-scale, multi-parameter database, we first link the low- and high-resolution geophysical data via a stochastic downscaling procedure. This is followed by relating the downscaled geophysical data to the high-resolution hydraulic conductivity distribution. After outlining the general methodology of the approach, we demonstrate its application to a realistic synthetic example where we consider as data high-resolution measurements of the hydraulic and electrical conductivities at a small number of borehole locations, as well as spatially exhaustive, low-resolution estimates of the electrical conductivity obtained from surface-based electrical resistivity tomography. The different stochastic realizations of the hydraulic conductivity field obtained using our procedure are validated by comparing their solute transport behaviour with that of the underlying ?true? hydraulic conductivity field. We find that, even in the presence of strong subsurface heterogeneity, our proposed procedure allows for the generation of faithful representations of the regional-scale hydraulic conductivity structure and reliable predictions of solute transport over long, regional-scale distances.

Clustering and hot spot detection in socio-economic spatio-temporal data

Distribution of socio-economic features in urban space is an important source of information for land and transportation planning. The metropolization phenomenon has changed the distribution of types of professions in space and has given birth to different spatial patterns that the urban planner must know in order to plan a sustainable city. Such distributions can be discovered by statistical and learning algorithms through different methods. In this paper, an unsupervised classification method and a cluster detection method are discussed and applied to analyze the socio-economic structure of Switzerland. The unsupervised classification method, based on Ward's classification and self-organized maps, is used to classify the municipalities of the country and allows to reduce a highly-dimensional input information to interpret the socio-economic landscape. The cluster detection method, the spatial scan statistics, is used in a more specific manner in order to detect hot spots of certain types of service activities. The method is applied to the distribution services in the agglomeration of Lausanne. Results show the emergence of new centralities and can be analyzed in both transportation and social terms.

Red cell distribution width does not predict stroke severity or functional outcome.

INTRODUCTION: Red cell distribution width was recently identified as a predictor of cardiovascular and all-cause mortality in patients with previous stroke. Red cell distribution width is also higher in patients with stroke compared with those without. However, there are no data on the association of red cell distribution width, assessed during the acute phase of ischemic stroke, with stroke severity and functional outcome. In the present study, we sought to investigate this relationship and ascertain the main determinants of red cell distribution width in this population. METHODS: We used data from the Acute Stroke Registry and Analysis of Lausanne for patients between January 2003 and December 2008. Red cell distribution width was generated at admission by the Sysmex XE-2100 automated cell counter from ethylene diamine tetraacetic acid blood samples stored at room temperature until measurement. An χ(2) -test was performed to compare frequencies of categorical variables between different red cell distribution width quartiles, and one-way analysis of variance for continuous variables. The effect of red cell distribution width on severity and functional outcome was investigated in univariate and multivariate robust regression analysis. Level of significance was set at 95%. RESULTS: There were 1504 patients (72±15·76 years, 43·9% females) included in the analysis. Red cell distribution width was significantly associated to NIHSS (β-value=0·24, P=0·01) and functional outcome (odds ratio=10·73 for poor outcome, P<0·001) at univariate analysis but not multivariate. Prehospital Rankin score (β=0·19, P<0·001), serum creatinine (β=0·008, P<0·001), hemoglobin (β=-0·009, P<0·001), mean platelet volume (β=0·09, P<0·05), age (β=0·02, P<0·001), low ejection fraction (β=0·66, P<0·001) and antihypertensive treatment (β=0·32, P<0·001) were independent determinants of red cell distribution width. CONCLUSIONS: Red cell distribution width, assessed during the early phase of acute ischemic stroke, does not predict severity or functional outcome.

Use of high-resolution geophysical data to characterize heterogeneous aquifers: influence of data integration method on hydrological predictions

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. The investigation of exactly how much benefit can be brought by geophysical data in terms of its effect on hydrological predictions, however, has received considerably less attention in the literature. Here, we examine the potential hydrological benefits brought by a recently introduced simulated annealing (SA) conditional stochastic simulation method designed for the assimilation of diverse hydrogeophysical data sets. We consider the specific case of integrating crosshole ground-penetrating radar (GPR) and borehole porosity log data to characterize the porosity distribution in saturated heterogeneous aquifers. In many cases, porosity is linked to hydraulic conductivity and thus to flow and transport behavior. To perform our evaluation, we first generate a number of synthetic porosity fields exhibiting varying degrees of spatial continuity and structural complexity. Next, we simulate the collection of crosshole GPR data between several boreholes in these fields, and the collection of porosity log data at the borehole locations. The inverted GPR data, together with the porosity logs, are then used to reconstruct the porosity field using the SA-based method, along with a number of other more elementary approaches. Assuming that the grid-cell-scale relationship between porosity and hydraulic conductivity is unique and known, the porosity realizations are then used in groundwater flow and contaminant transport simulations to assess the benefits and limitations of the different approaches.

Comparison of plantar pressure distribution in adolescent runners at low vs. high running velocity.

This study aimed to compare foot plantar pressure distribution while jogging and running in highly trained adolescent runners. Eleven participants performed two constant-velocity running trials either at jogging (11.2 ± 0.9 km/h) or running (17.8 ± 1.4 km/h) pace on a treadmill. Contact area (CA in cm(2)), maximum force (F(max) in N), peak pressure (PP in kPa), contact time (CT in ms), and relative load (force time integral in each individual region divided by the force time integral for the total plantar foot surface, in %) were measured in nine regions of the right foot using an in-shoe plantar pressure device. Under the whole foot, CA, F(max) and PP were lower in jogging than in running (-1.2% [p<0.05], -12.3% [p<0.001] and -15.1% [p<0.01] respectively) whereas CT was higher (+20.1%; p<0.001). Interestingly, we found an increase in relative load under the medial and central forefoot regions while jogging (+6.7% and +3.7%, respectively; [p<0.05]), while the relative load under the lesser toes (-8.4%; p<0.05) was reduced. In order to prevent overloading of the metatarsals in adolescent runners, excessive mileage at jogging pace should be avoided.

What spatial data do we need to develop global mammal conservation strategies?

Spatial data on species distributions are available in two main forms, point locations and distribution maps (polygon ranges and grids). The first are often temporally and spatially biased, and too discontinuous, to be useful (untransformed) in spatial analyses. A variety of modelling approaches are used to transform point locations into maps. We discuss the attributes that point location data and distribution maps must satisfy in order to be useful in conservation planning. We recommend that before point location data are used to produce and/or evaluate distribution models, the dataset should be assessed under a set of criteria, including sample size, age of data, environmental/geographical coverage, independence, accuracy, time relevance and (often forgotten) representation of areas of permanent and natural presence of the species. Distribution maps must satisfy additional attributes if used for conservation analyses and strategies, including minimizing commission and omission errors, credibility of the source/assessors and availability for public screening. We review currently available databases for mammals globally and show that they are highly variable in complying with these attributes. The heterogeneity and weakness of spatial data seriously constrain their utility to global and also sub-global scale conservation analyses.

Distribution of artifactual gas on post-mortem multidetector computed tomography (MDCT).

PURPOSE: We investigated the incidence and distribution of post-mortem gas detected with multidetector computed tomography (MDCT) to identify factors that could distinguish artifactual gas from cardiac air embolism. MATERIAL AND METHODS: MDCT data of 119 cadavers were retrospectively examined. Gas was semiquantitatively assessed in selected blood vessels, organs, and body spaces (82 total sites). RESULTS: Seventy-four of the 119 cadavers displayed gas (62.2%; CI 95% 52.8-70.9), and 56 (75.7%) displayed gas in the heart. Most gas was detected in the hepatic parenchyma (40%), right heart (38% ventricle, 35% atrium), inferior vena cava (30% infrarenally, 26% suprarenally), hepatic veins (26% left, 29% middle, 22% right), and portal spaces (29%). Male cadavers displayed gas more frequently than female cadavers. Gas was detected 5-84 hours after death; therefore, the post-mortem interval could not reliably predict gas distribution (rho = 0.719, p < 0.0001). We found that a large amount of putrefaction-generated gas in the right heart was associated with aggregated gas bubbles in the hepatic parenchyma (sensitivity = 100%, specificity = 89.7%). In contrast, gas in the left heart (sensitivity = 41.7%, specificity = 100%) or in periumbilical subcutaneous tissues (sensitivity = 50%, specificity = 96.3%) could not predict gas due to putrefaction. CONCLUSION: This study is the first to show that the appearance of post-mortem gas follows a specific distribution pattern. An association between intracardiac gas and hepatic parenchymal gas could distinguish between post-mortem-generated gas and vital air embolism. We propose that this finding provides a key for diagnosing death due to cardiac air embolism.

Horizontal, but not vertical, biotic interactions affect fine-scale plant distribution patterns in a low-energy system

Studies of species range determinants have traditionally focused on abiotic variables (typically climatic conditions), and therefore the recent explicit consideration of biotic interactions represents an important advance in the field. While these studies clearly support the role of biotic interactions in shaping species distributions, most examine only the influence of a single species and/or a single interaction, failing to account for species being subject to multiple concurrent interactions. By fitting species distribution models (SDMs), we examine the influence of multiple vertical (i.e., grazing, trampling, and manuring by mammalian herbivores) and horizontal (i.e., competition and facilitation; estimated from the cover of dominant plant species) interspecific interactions on the occurrence and cover of 41 alpine tundra plant species. Adding plant-plant interactions to baseline SDMs (using five field-quantified abiotic variables) significantly improved models' predictive power for independent data, while herbivore-related variables had only a weak influence. Overall, abiotic variables had the strongest individual contributions to the distribution of alpine tundra plants, with the importance of horizontal interaction variables exceeding that of vertical interaction variables. These results were consistent across three modeling techniques, for both species occurrence and cover, demonstrating the pattern to be robust. Thus, the explicit consideration of multiple biotic interactions reveals that plant-plant interactions exert control over the fine-scale distribution of vascular species that is comparable to abiotic drivers and considerably stronger than herbivores in this low-energy system.

Selective distribution of lactate dehydrogenase isoenzymes in neurons and astrocytes of human brain.

In vertebrates, the interconversion of lactate and pyruvate is catalyzed by the enzyme lactate dehydrogenase. Two distinct subunits combine to form the five tetrameric isoenzymes of lactate dehydrogenase. The LDH-5 subunit (muscle type) has higher maximal velocity (Vmax) and is present in glycolytic tissues, favoring the formation of lactate from pyruvate. The LDH-1 subunit (heart type) is inhibited by pyruvate and therefore preferentially drives the reaction toward the production of pyruvate. There is mounting evidence indicating that during activation the brain resorts to the transient glycolytic processing of glucose. Indeed, transient lactate formation during physiological stimulation has been shown by 1H-magnetic resonance spectroscopy. However, since whole-brain arteriovenous studies under basal conditions indicate a virtually complete oxidation of glucose, the vast proportion of the lactate transiently formed during activation is likely to be oxidized. These in vivo data suggest that lactate may be formed in certain cells and oxidized in others. We therefore set out to determine whether the two isoforms of lactate dehydrogenase are localized to selective cell types in the human brain. We report here the production and characterization of two rat antisera, specific for the LDH-5 and LDH-1 subunits of lactate dehydrogenase, respectively. Immunohistochemical, immunodot, and western-blot analyses show that these antisera specifically recognize their homologous antigens. Immunohistochemistry on 10 control cases demonstrated a differential cellular distribution between both subunits in the hippocampus and occipital cortex: neurons are exclusively stained with the anti-LDH1 subunit while astrocytes are stained by both antibodies. These observations support the notion of a regulated lactate flux between astrocytes and neurons.

Distribution des durées de séjour par Diagnosis Related Group

Ce cahier fournit les principales informations concernant les durées de séjour et le nombre de sorties observées dans 33 hôpitaux suisses pour l'années 1984. La description des clientèles hospitalières est fondée sur les "Diagnosis Related Groups" (DRG), qui forment une classification de 472 groupes de patients hospitalisés. [Auteurs, p. 1]

A stratified approach for modeling the distribution of a threatened ant species in the Swiss National Park

We present models predicting the potential distribution of a threatened ant species, Formica exsecta Nyl., in the Swiss National Park ( SNP). Data to fit the models have been collected according to a random-stratified design with an equal number of replicates per stratum. The basic aim of such a sampling strategy is to allow the formal testing of biological hypotheses about those factors most likely to account for the distribution of the modeled species. The stratifying factors used in this study were: vegetation, slope angle and slope aspect, the latter two being used as surrogates of solar radiation, considered one of the basic requirements of F. exsecta. Results show that, although the basic stratifying predictors account for more than 50% of the deviance, the incorporation of additional non-spatially explicit predictors into the model, as measured in the field, allows for an increased model performance (up to nearly 75%). However, this was not corroborated by permutation tests. Implementation on a national scale was made for one model only, due to the difficulty of obtaining similar predictors on this scale. The resulting map on the national scale suggests that the species might once have had a broader distribution in Switzerland. Reasons for its particular abundance within the SNP might possibly be related to habitat fragmentation and vegetation transformation outside the SNP boundaries.

The truncated mean of an asymmetric distribution

This paper investigates a simple procedure to estimate robustly the mean of an asymmetric distribution. The procedure removes the observations which are larger or smaller than certain limits and takes the arithmetic mean of the remaining observations, the limits being determined with the help of a parametric model, e.g., the Gamma, the Weibull or the Lognormal distribution. The breakdown point, the influence function, the (asymptotic) variance, and the contamination bias of this estimator are explored and compared numerically with those of competing estimates.

Monitoring of illicit pill distribution networks using an image collection exploration framework

This paper proposes a novel approach for the analysis of illicit tablets based on their visual characteristics. In particular, the paper concentrates on the problem of ecstasy pill seizure profiling and monitoring. The presented method extracts the visual information from pill images and builds a representation of it, i.e. it builds a pill profile based on the pill visual appearance. Different visual features are used to build different image similarity measures, which are the basis for a pill monitoring strategy based on both discriminative and clustering models. The discriminative model permits to infer whether two pills come from the same seizure, while the clustering models groups of pills that share similar visual characteristics. The resulting clustering structure allows to perform a visual identification of the relationships between different seizures. The proposed approach was evaluated using a data set of 621 Ecstasy pill pictures. The results demonstrate that this is a feasible and cost effective method for performing pill profiling and monitoring.

Molecular phylogeny and evolution of Sorex shrews (Soricidae: insectivora) inferred from mitochondrial DNA sequence data.

Shrews of the genus Sorex are characterized by a Holarctic distribution, and relationships among extant taxa have never been fully resolved. Phylogenies have been proposed based on morphological, karyological, and biochemical comparisons, but these analyses often produced controversial and contradictory results. Phylogenetic analyses of partial mitochondrial cytochrome b gene sequences (1011 bp) were used to examine the relationships among 27 Sorex species. The molecular data suggest that Sorex comprises two major monophyletic lineages, one restricted mostly to the New World and one with a primarily Palearctic distribution. Furthermore, several sister-species relationships are revealed by the analysis. Based on the split between the Soricinae and Crocidurinae subfamilies, we used a 95% confidence interval for both the calibration of a molecular clock and the subsequent calculation of major diversification events within the genus Sorex. Our analysis does not support an unambiguous acceleration of the molecular clock in shrews, the estimated rate being similar to other estimates of mammalian mitochondrial clocks. In addition, the data presented here indicate that estimates from the fossil record greatly underestimate divergence dates among Sorex taxa.

The role of biotic interactions in shaping distributions and realised assemblages of species: implications for species distribution modelling.

Predicting which species will occur together in the future, and where, remains one of the greatest challenges in ecology, and requires a sound understanding of how the abiotic and biotic environments interact with dispersal processes and history across scales. Biotic interactions and their dynamics influence species' relationships to climate, and this also has important implications for predicting future distributions of species. It is already well accepted that biotic interactions shape species' spatial distributions at local spatial extents, but the role of these interactions beyond local extents (e.g. 10 km(2) to global extents) are usually dismissed as unimportant. In this review we consolidate evidence for how biotic interactions shape species distributions beyond local extents and review methods for integrating biotic interactions into species distribution modelling tools. Drawing upon evidence from contemporary and palaeoecological studies of individual species ranges, functional groups, and species richness patterns, we show that biotic interactions have clearly left their mark on species distributions and realised assemblages of species across all spatial extents. We demonstrate this with examples from within and across trophic groups. A range of species distribution modelling tools is available to quantify species environmental relationships and predict species occurrence, such as: (i) integrating pairwise dependencies, (ii) using integrative predictors, and (iii) hybridising species distribution models (SDMs) with dynamic models. These methods have typically only been applied to interacting pairs of species at a single time, require a priori ecological knowledge about which species interact, and due to data paucity must assume that biotic interactions are constant in space and time. To better inform the future development of these models across spatial scales, we call for accelerated collection of spatially and temporally explicit species data. Ideally, these data should be sampled to reflect variation in the underlying environment across large spatial extents, and at fine spatial resolution. Simplified ecosystems where there are relatively few interacting species and sometimes a wealth of existing ecosystem monitoring data (e.g. arctic, alpine or island habitats) offer settings where the development of modelling tools that account for biotic interactions may be less difficult than elsewhere.