An empirical hierarchical Bayesian unification of occupational exposure assessment methods.

In occupational exposure assessment of airborne contaminants, exposure levels can either be estimated through repeated measurements of the pollutant concentration in air, expert judgment or through exposure models that use information on the conditions of exposure as input. In this report, we propose an empirical hierarchical Bayesian model to unify these approaches. Prior to any measurement, the hygienist conducts an assessment to generate prior distributions of exposure determinants. Monte-Carlo samples from these distributions feed two level-2 models: a physical, two-compartment model, and a non-parametric, neural network model trained with existing exposure data. The outputs of these two models are weighted according to the expert's assessment of their relevance to yield predictive distributions of the long-term geometric mean and geometric standard deviation of the worker's exposure profile (level-1 model). Bayesian inferences are then drawn iteratively from subsequent measurements of worker exposure. Any traditional decision strategy based on a comparison with occupational exposure limits (e.g. mean exposure, exceedance strategies) can then be applied. Data on 82 workers exposed to 18 contaminants in 14 companies were used to validate the model with cross-validation techniques. A user-friendly program running the model is available upon request.

Further insights on the French WISC-IV factor structure through Bayesian structural equation modeling

The interpretation of the Wechsler Intelligence Scale for Children-Fourth Edition (WISC-IV) is based on a 4-factor model, which is only partially compatible with the mainstream Cattell-Horn-Carroll (CHC) model of intelligence measurement. The structure of cognitive batteries is frequently analyzed via exploratory factor analysis and/or confirmatory factor analysis. With classical confirmatory factor analysis, almost all crossloadings between latent variables and measures are fixed to zero in order to allow the model to be identified. However, inappropriate zero cross-loadings can contribute to poor model fit, distorted factors, and biased factor correlations; most important, they do not necessarily faithfully reflect theory. To deal with these methodological and theoretical limitations, we used a new statistical approach, Bayesian structural equation modeling (BSEM), among a sample of 249 French-speaking Swiss children (8-12 years). With BSEM, zero-fixed cross-loadings between latent variables and measures are replaced by approximate zeros, based on informative, small-variance priors. Results indicated that a direct hierarchical CHC-based model with 5 factors plus a general intelligence factor better represented the structure of the WISC-IV than did the 4-factor structure and the higher order models. Because a direct hierarchical CHC model was more adequate, it was concluded that the general factor should be considered as a breadth rather than a superordinate factor. Because it was possible for us to estimate the influence of each of the latent variables on the 15 subtest scores, BSEM allowed improvement of the understanding of the structure of intelligence tests and the clinical interpretation of the subtest scores.

Statistical Modeling of the Eye for Multimodal Treatment Planning for External Beam Radiation Therapy of Intraocular Tumors.

PURPOSE: Ocular anatomy and radiation-associated toxicities provide unique challenges for external beam radiation therapy. For treatment planning, precise modeling of organs at risk and tumor volume are crucial. Development of a precise eye model and automatic adaptation of this model to patients' anatomy remain problematic because of organ shape variability. This work introduces the application of a 3-dimensional (3D) statistical shape model as a novel method for precise eye modeling for external beam radiation therapy of intraocular tumors. METHODS AND MATERIALS: Manual and automatic segmentations were compared for 17 patients, based on head computed tomography (CT) volume scans. A 3D statistical shape model of the cornea, lens, and sclera as well as of the optic disc position was developed. Furthermore, an active shape model was built to enable automatic fitting of the eye model to CT slice stacks. Cross-validation was performed based on leave-one-out tests for all training shapes by measuring dice coefficients and mean segmentation errors between automatic segmentation and manual segmentation by an expert. RESULTS: Cross-validation revealed a dice similarity of 95% ± 2% for the sclera and cornea and 91% ± 2% for the lens. Overall, mean segmentation error was found to be 0.3 ± 0.1 mm. Average segmentation time was 14 ± 2 s on a standard personal computer. CONCLUSIONS: Our results show that the solution presented outperforms state-of-the-art methods in terms of accuracy, reliability, and robustness. Moreover, the eye model shape as well as its variability is learned from a training set rather than by making shape assumptions (eg, as with the spherical or elliptical model). Therefore, the model appears to be capable of modeling nonspherically and nonelliptically shaped eyes.

Scale-specific sex-biased dispersal in the Valais shrew unveiled by genetic variation on the Y chromosome, autosomes, and mitochondrial DNA.

We investigated sex specificities in the evolutionary processes shaping Y chromosome, autosomes, and mitochondrial DNA patterns of genetic structure in the Valais shrew (Sorex antinorii), a mountain dwelling species with a hierarchical distribution. Both hierarchical analyses of variance and isolation-by-distance analyses revealed patterns of population structure that were not consistent across maternal, paternal, and biparentally inherited markers. Differentiation on a Y microsatellite was lower than expected from the comparison with autosomal microsatellites and mtDNA, and it was mostly due to genetic variance among populations within valleys, whereas the opposite was observed on other markers. In addition, there was no pattern of isolation by distance for the Y, whereas there was strong isolation by distance on mtDNA and autosomes. We use a hierarchical island model of coancestry dynamics to discuss the relative roles of the microevolutionary forces that may induce such patterns. We conclude that sex-biased dispersal is the most important driver of the observed genetic structure, but with an intriguing twist: it seems that dispersal is strongly male biased at large spatial scale, whereas it is mildly biased in favor of females at local scale. These results add to recent reports of scale-specific sex-biased dispersal patterns, and emphasize the usefulness of the Y chromosome in conjunction with mtDNA and autosomes to infer sex specificities.

Assessing the added value of wastewater-based epidemiology to monitor illicit drug use

Wastewater-based epidemiology consists in acquiring relevant information about the lifestyle and health status of the population through the analysis of wastewater samples collected at the influent of a wastewater treatment plant. Whilst being a very young discipline, it has experienced an astonishing development since its firs application in 2005. The possibility to gather community-wide information about drug use has been among the major field of application. The wide resonance of the first results sparked the interest of scientists from various disciplines. Since then, research has broadened in innumerable directions. Although being praised as a revolutionary approach, there was a need to critically assess its added value, with regard to the existing indicators used to monitor illicit drug use. The main, and explicit, objective of this research was to evaluate the added value of wastewater-based epidemiology with regards to two particular, although interconnected, dimensions of illicit drug use. The first is related to trying to understand the added value of the discipline from an epidemiological, or societal, perspective. In other terms, to evaluate if and how it completes our current vision about the extent of illicit drug use at the population level, and if it can guide the planning of future prevention measures and drug policies. The second dimension is the criminal one, with a particular focus on the networks which develop around the large demand in illicit drugs. The goal here was to assess if wastewater-based epidemiology, combined to indicators stemming from the epidemiological dimension, could provide additional clues about the structure of drug distribution networks and the size of their market. This research had also an implicit objective, which focused on initiating the path of wastewater- based epidemiology at the Ecole des Sciences Criminelles of the University of Lausanne. This consisted in gathering the necessary knowledge about the collection, preparation, and analysis of wastewater samples and, most importantly, to understand how to interpret the acquired data and produce useful information. In the first phase of this research, it was possible to determine that ammonium loads, measured directly in the wastewater stream, could be used to monitor the dynamics of the population served by the wastewater treatment plant. Furthermore, it was shown that on the long term, the population did not have a substantial impact on consumption patterns measured through wastewater analysis. Focussing on methadone, for which precise prescription data was available, it was possible to show that reliable consumption estimates could be obtained via wastewater analysis. This allowed to validate the selected sampling strategy, which was then used to monitor the consumption of heroin, through the measurement of morphine. The latter, in combination to prescription and sales data, provided estimates of heroin consumption in line with other indicators. These results, combined to epidemiological data, highlighted the good correspondence between measurements and expectations and, furthermore, suggested that the dark figure of heroin users evading harm-reduction programs, which would thus not be measured by conventional indicators, is likely limited. In the third part, which consisted in a collaborative study aiming at extensively investigating geographical differences in drug use, wastewater analysis was shown to be a useful complement to existing indicators. In particular for stigmatised drugs, such as cocaine and heroin, it allowed to decipher the complex picture derived from surveys and crime statistics. Globally, it provided relevant information to better understand the drug market, both from an epidemiological and repressive perspective. The fourth part focused on cannabis and on the potential of combining wastewater and survey data to overcome some of their respective limitations. Using a hierarchical inference model, it was possible to refine current estimates of cannabis prevalence in the metropolitan area of Lausanne. Wastewater results suggested that the actual prevalence is substantially higher compared to existing figures, thus supporting the common belief that surveys tend to underestimate cannabis use. Whilst being affected by several biases, the information collected through surveys allowed to overcome some of the limitations linked to the analysis of cannabis markers in wastewater (i.e., stability and limited excretion data). These findings highlighted the importance and utility of combining wastewater-based epidemiology to existing indicators about drug use. Similarly, the fifth part of the research was centred on assessing the potential uses of wastewater-based epidemiology from a law enforcement perspective. Through three concrete examples, it was shown that results from wastewater analysis can be used to produce highly relevant intelligence, allowing drug enforcement to assess the structure and operations of drug distribution networks and, ultimately, guide their decisions at the tactical and/or operational level. Finally, the potential to implement wastewater-based epidemiology to monitor the use of harmful, prohibited and counterfeit pharmaceuticals was illustrated through the analysis of sibutramine, and its urinary metabolite, in wastewater samples. The results of this research have highlighted that wastewater-based epidemiology is a useful and powerful approach with numerous scopes. Faced with the complexity of measuring a hidden phenomenon like illicit drug use, it is a major addition to the panoply of existing indicators. -- L'épidémiologie basée sur l'analyse des eaux usées (ou, selon sa définition anglaise, « wastewater-based epidemiology ») consiste en l'acquisition d'informations portant sur le mode de vie et l'état de santé d'une population via l'analyse d'échantillons d'eaux usées récoltés à l'entrée des stations d'épuration. Bien qu'il s'agisse d'une discipline récente, elle a vécu des développements importants depuis sa première mise en oeuvre en 2005, notamment dans le domaine de l'analyse des résidus de stupéfiants. Suite aux retombées médiatiques des premiers résultats de ces analyses de métabolites dans les eaux usées, de nombreux scientifiques provenant de différentes disciplines ont rejoint les rangs de cette nouvelle discipline en développant plusieurs axes de recherche distincts. Bien que reconnu pour son coté objectif et révolutionnaire, il était nécessaire d'évaluer sa valeur ajoutée en regard des indicateurs couramment utilisés pour mesurer la consommation de stupéfiants. En se focalisant sur deux dimensions spécifiques de la consommation de stupéfiants, l'objectif principal de cette recherche était focalisé sur l'évaluation de la valeur ajoutée de l'épidémiologie basée sur l'analyse des eaux usées. La première dimension abordée était celle épidémiologique ou sociétale. En d'autres termes, il s'agissait de comprendre si et comment l'analyse des eaux usées permettait de compléter la vision actuelle sur la problématique, ainsi que déterminer son utilité dans la planification des mesures préventives et des politiques en matière de stupéfiants actuelles et futures. La seconde dimension abordée était celle criminelle, en particulier, l'étude des réseaux qui se développent autour du trafic de produits stupéfiants. L'objectif était de déterminer si cette nouvelle approche combinée aux indicateurs conventionnels, fournissait de nouveaux indices quant à la structure et l'organisation des réseaux de distribution ainsi que sur les dimensions du marché. Cette recherche avait aussi un objectif implicite, développer et d'évaluer la mise en place de l'épidémiologie basée sur l'analyse des eaux usées. En particulier, il s'agissait d'acquérir les connaissances nécessaires quant à la manière de collecter, traiter et analyser des échantillons d'eaux usées, mais surtout, de comprendre comment interpréter les données afin d'en extraire les informations les plus pertinentes. Dans la première phase de cette recherche, il y pu être mis en évidence que les charges en ammonium, mesurées directement dans les eaux usées permettait de suivre la dynamique des mouvements de la population contributrice aux eaux usées de la station d'épuration de la zone étudiée. De plus, il a pu être démontré que, sur le long terme, les mouvements de la population n'avaient pas d'influence substantielle sur le pattern de consommation mesuré dans les eaux usées. En se focalisant sur la méthadone, une substance pour laquelle des données précises sur le nombre de prescriptions étaient disponibles, il a pu être démontré que des estimations exactes sur la consommation pouvaient être tirées de l'analyse des eaux usées. Ceci a permis de valider la stratégie d'échantillonnage adoptée, qui, par le bais de la morphine, a ensuite été utilisée pour suivre la consommation d'héroïne. Combinée aux données de vente et de prescription, l'analyse de la morphine a permis d'obtenir des estimations sur la consommation d'héroïne en accord avec des indicateurs conventionnels. Ces résultats, combinés aux données épidémiologiques ont permis de montrer une bonne adéquation entre les projections des deux approches et ainsi démontrer que le chiffre noir des consommateurs qui échappent aux mesures de réduction de risque, et qui ne seraient donc pas mesurés par ces indicateurs, est vraisemblablement limité. La troisième partie du travail a été réalisée dans le cadre d'une étude collaborative qui avait pour but d'investiguer la valeur ajoutée de l'analyse des eaux usées à mettre en évidence des différences géographiques dans la consommation de stupéfiants. En particulier pour des substances stigmatisées, telles la cocaïne et l'héroïne, l'approche a permis d'objectiver et de préciser la vision obtenue avec les indicateurs traditionnels du type sondages ou les statistiques policières. Globalement, l'analyse des eaux usées s'est montrée être un outil très utile pour mieux comprendre le marché des stupéfiants, à la fois sous l'angle épidémiologique et répressif. La quatrième partie du travail était focalisée sur la problématique du cannabis ainsi que sur le potentiel de combiner l'analyse des eaux usées aux données de sondage afin de surmonter, en partie, leurs limitations. En utilisant un modèle d'inférence hiérarchique, il a été possible d'affiner les actuelles estimations sur la prévalence de l'utilisation de cannabis dans la zone métropolitaine de la ville de Lausanne. Les résultats ont démontré que celle-ci est plus haute que ce que l'on s'attendait, confirmant ainsi l'hypothèse que les sondages ont tendance à sous-estimer la consommation de cannabis. Bien que biaisés, les données récoltées par les sondages ont permis de surmonter certaines des limitations liées à l'analyse des marqueurs du cannabis dans les eaux usées (i.e., stabilité et manque de données sur l'excrétion). Ces résultats mettent en évidence l'importance et l'utilité de combiner les résultats de l'analyse des eaux usées aux indicateurs existants. De la même façon, la cinquième partie du travail était centrée sur l'apport de l'analyse des eaux usées du point de vue de la police. Au travers de trois exemples, l'utilisation de l'indicateur pour produire du renseignement concernant la structure et les activités des réseaux de distribution de stupéfiants, ainsi que pour guider les choix stratégiques et opérationnels de la police, a été mise en évidence. Dans la dernière partie, la possibilité d'utiliser cette approche pour suivre la consommation de produits pharmaceutiques dangereux, interdits ou contrefaits, a été démontrée par l'analyse dans les eaux usées de la sibutramine et ses métabolites. Les résultats de cette recherche ont mis en évidence que l'épidémiologie par l'analyse des eaux usées est une approche pertinente et puissante, ayant de nombreux domaines d'application. Face à la complexité de mesurer un phénomène caché comme la consommation de stupéfiants, la valeur ajoutée de cette approche a ainsi pu être démontrée.

A hierarchical approach to model parameter optimization for developmental systems.

In the context of Systems Biology, computer simulations of gene regulatory networks provide a powerful tool to validate hypotheses and to explore possible system behaviors. Nevertheless, modeling a system poses some challenges of its own: especially the step of model calibration is often difficult due to insufficient data. For example when considering developmental systems, mostly qualitative data describing the developmental trajectory is available while common calibration techniques rely on high-resolution quantitative data. Focusing on the calibration of differential equation models for developmental systems, this study investigates different approaches to utilize the available data to overcome these difficulties. More specifically, the fact that developmental processes are hierarchically organized is exploited to increase convergence rates of the calibration process as well as to save computation time. Using a gene regulatory network model for stem cell homeostasis in Arabidopsis thaliana the performance of the different investigated approaches is evaluated, documenting considerable gains provided by the proposed hierarchical approach.

Validation of the French version of the Hierarchical Personality Inventory for Children (HiPIC): Influence of gender and age on personality traits from 8 to 12 years

The study was designed to investigate the psychometric properties of the French version and the cross-language replicability of the Hierarchical Personality Inventory for Children (HiPIC). The HiPIC is an instrument aimed at assessing the five dimensions of the Five-Factor Model for Children. Subjects were 552 children aged between 8 and 12 years, rated by one or both parents. At the domain level, reliability ranged from .83 to .93 and at the facet level, reliability ranged from .69 to .89. Differences between genders were congruent with those found in the Dutch sample. Girls scored higher on Benevolence and Conscientiousness. Age was negatively correlated with Extraversion and Imagination. For girls, we also observed a decrease of Emotional Stability. A series of exploratory factor analyses confirmed the overall five-factor structure for girls and boys. Targeted factor analyses and congruence coefficients revealed high cross-language replicability at the domain and at the facet levels. The results showed that the French version of the HiPIC is a reliable and valid instrument for assessing personality with children and has a particularly high cross-language replicability.

Will climate change drive alien invasive plants into areas of high protection value? An improved model-based regional assessment to prioritise the management of invasions.

Species distribution models (SDMs) studies suggest that, without control measures, the distribution of many alien invasive plant species (AIS) will increase under climate and land-use changes. Due to limited resources and large areas colonised by invaders, management and monitoring resources must be prioritised. Choices depend on the conservation value of the invaded areas and can be guided by SDM predictions. Here, we use a hierarchical SDM framework, complemented by connectivity analysis of AIS distributions, to evaluate current and future conflicts between AIS and high conservation value areas. We illustrate the framework with three Australian wattle (Acacia) species and patterns of conservation value in Northern Portugal. Results show that protected areas will likely suffer higher pressure from all three Acacia species under future climatic conditions. Due to this higher predicted conflict in protected areas, management might be prioritised for Acacia dealbata and Acacia melanoxylon. Connectivity of AIS suitable areas inside protected areas is currently lower than across the full study area, but this would change under future environmental conditions. Coupled SDM and connectivity analysis can support resource prioritisation for anticipation and monitoring of AIS impacts. However, further tests of this framework over a wide range of regions and organisms are still required before wide application.

Animal model to compare the effects of suture technique on cross-sectional compliance on end-to-side anastomoses.

OBJECTIVE: An animal model has been developed to compare the effects of suture technique on the luminal dimensions and compliance of end-to-side vascular anastomoses. METHODS: Carotid and internal mammalian arteries (IMAs) were exposed in three pigs (90 kg). IMAs were sectioned distally to perform end-to-side anastomoses on carotid arteries. One anastomosis was performed with 7/0 polypropylene running suture. The other was performed with the automated suture delivery device (Perclose/Abbott Labs Inc.) that makes a 7/0 polypropylene interrupted suture. Four piezoelectric crystals were sutured on toe, heel and both lateral sides of each anastomosis to measure anastomotic axes. Anastomotic cross-sectional area (CSAA) was calculated with: CSAA = pi x mM/4 where m and M are the minor and major axes of the elliptical anastomosis. Cross-sectional anastomotic compliance (CSAC) was calculated as CSAC=Delta CSAA/Delta P where Delta P is the mean pulse pressure and Delta CSAA is the mean CSAA during cardiac cycle. RESULTS: We collected a total of 1200000 pressure-length data per animal. For running suture we had a mean systolic CSAA of 26.94+/-0.4 mm(2) and a mean CSAA in diastole of 26.30+/-0.5 mm(2) (mean Delta CSAA was 0.64 mm(2)). CSAC for running suture was 4.5 x 10(-6)m(2)/kPa. For interrupted suture we had a mean CSAA in systole of 21.98+/-0.2 mm(2) and a mean CSAA in diastole of 17.38+/-0.3 mm(2) (mean Delta CSAA was 4.6+/-0.1 mm(2)). CSAC for interrupted suture was 11 x 10(-6) m(2)/kPa. CONCLUSIONS: This model, even with some limitations, can be a reliable source of information improving the outcome of vascular anastomoses. The study demonstrates that suture technique has a substantial effect on cross-sectional anastomotic compliance of end-to-side anastomoses. Interrupted suture may maximise the anastomotic lumen and provides a considerably higher CSAC than continuous suture, that reduces flow turbulence, shear stress and intimal hyperplasia. The Heartflo anastomosis device is a reliable instrument that facilitates performance of interrupted suture anastomoses.

The hierarchical structures of the NEO-PI-R and the 16PF5

The present study compares the higher-level dimensions and the hierarchical structures of the fifth edition of the 16 PF with those of the NEO PI-R. Both inventories measure personality according to five higher-level dimensions. These inventories were however constructed according to different methods (bottom-up vs. top-down). 386 participants filled out both questionnaires. Correlations, regressions and canonical correlations made it possible to compare the inventories. As expected they roughly measure the same aspects of personality. There is a coherent association among four of the five dimensions measured in the tests. However Agreeableness, the remaining dimension in the NEO PI-R, is not represented in the 16 PF 5. Our analyses confirmed the hierarchical structures of both instruments, but this confirmation was more complete in the case of the NEO PI-R. Indeed, a parallel analysis indicated that a four-factor solution should be considered in the case of the 16 PF 5. On the other hand, the NEO PI-R's five-factor solution was confirmed. The top-down construction of this instrument seems to make for a more legible structure. Of the two five-dimension constructs, the NEO PI-R thus seems the more reliable. This confirms the relevance of the Five Factor Model of personality.

Overcoming the rare species modelling paradox: test of a novel hierarchical framework with an Iberian endemic plant

Rare species have restricted geographic ranges, habitat specialization, and/or small population sizes. Datasets on rare species distribution usually have few observations, limited spatial accuracy and lack of valid absences; conversely they provide comprehensive views of species distributions allowing to realistically capture most of their realized environmental niche. Rare species are the most in need of predictive distribution modelling but also the most difficult to model. We refer to this contrast as the "rare species modelling paradox" and propose as a solution developing modelling approaches that deal with a sufficiently large set of predictors, ensuring that statistical models aren't overfitted. Our novel approach fulfils this condition by fitting a large number of bivariate models and averaging them with a weighted ensemble approach. We further propose that this ensemble forecasting is conducted within a hierarchic multi-scale framework. We present two ensemble models for a test species, one at regional and one at local scale, each based on the combination of 630 models. In both cases, we obtained excellent spatial projections, unusual when modelling rare species. Model results highlight, from a statistically sound approach, the effects of multiple drivers in a same modelling framework and at two distinct scales. From this added information, regional models can support accurate forecasts of range dynamics under climate change scenarios, whereas local models allow the assessment of isolated or synergistic impacts of changes in multiple predictors. This novel framework provides a baseline for adaptive conservation, management and monitoring of rare species at distinct spatial and temporal scales.

Sample size and statistical power in the hierarchical analysis of variance: applications in morphometry of the nervous system.

Analysis of variance is commonly used in morphometry in order to ascertain differences in parameters between several populations. Failure to detect significant differences between populations (type II error) may be due to suboptimal sampling and lead to erroneous conclusions; the concept of statistical power allows one to avoid such failures by means of an adequate sampling. Several examples are given in the morphometry of the nervous system, showing the use of the power of a hierarchical analysis of variance test for the choice of appropriate sample and subsample sizes. In the first case chosen, neuronal densities in the human visual cortex, we find the number of observations to be of little effect. For dendritic spine densities in the visual cortex of mice and humans, the effect is somewhat larger. A substantial effect is shown in our last example, dendritic segmental lengths in monkey lateral geniculate nucleus. It is in the nature of the hierarchical model that sample size is always more important than subsample size. The relative weight to be attributed to subsample size thus depends on the relative magnitude of the between observations variance compared to the between individuals variance.

Hierarchical modeling gave plausible estimates of associations between metabolic syndrome and components of antiretroviral therapy.

OBJECTIVE: Hierarchical modeling has been proposed as a solution to the multiple exposure problem. We estimate associations between metabolic syndrome and different components of antiretroviral therapy using both conventional and hierarchical models. STUDY DESIGN AND SETTING: We use discrete time survival analysis to estimate the association between metabolic syndrome and cumulative exposure to 16 antiretrovirals from four drug classes. We fit a hierarchical model where the drug class provides a prior model of the association between metabolic syndrome and exposure to each antiretroviral. RESULTS: One thousand two hundred and eighteen patients were followed for a median of 27 months, with 242 cases of metabolic syndrome (20%) at a rate of 7.5 cases per 100 patient years. Metabolic syndrome was more likely to develop in patients exposed to stavudine, but was less likely to develop in those exposed to atazanavir. The estimate for exposure to atazanavir increased from hazard ratio of 0.06 per 6 months' use in the conventional model to 0.37 in the hierarchical model (or from 0.57 to 0.81 when using spline-based covariate adjustment). CONCLUSION: These results are consistent with trials that show the disadvantage of stavudine and advantage of atazanavir relative to other drugs in their respective classes. The hierarchical model gave more plausible results than the equivalent conventional model.

Gene expression profiling reveals consistent differences between clinical samples of human leukaemias and their model cell lines.

Microarray gene expression profiles of fresh clinical samples of chronic myeloid leukaemia in chronic phase, acute promyelocytic leukaemia and acute monocytic leukaemia were compared with profiles from cell lines representing the corresponding types of leukaemia (K562, NB4, HL60). In a hierarchical clustering analysis, all clinical samples clustered separately from the cell lines, regardless of leukaemic subtype. Gene ontology analysis showed that cell lines chiefly overexpressed genes related to macromolecular metabolism, whereas in clinical samples genes related to the immune response were abundantly expressed. These findings must be taken into consideration when conclusions from cell line-based studies are extrapolated to patients.

Two-dimensional probabilistic inversion of plane-wave electromagnetic data: Methodology, model constraints and joint inversion with electrical resistivity data

Probabilistic inversion methods based on Markov chain Monte Carlo (MCMC) simulation are well suited to quantify parameter and model uncertainty of nonlinear inverse problems. Yet, application of such methods to CPU-intensive forward models can be a daunting task, particularly if the parameter space is high dimensional. Here, we present a 2-D pixel-based MCMC inversion of plane-wave electromagnetic (EM) data. Using synthetic data, we investigate how model parameter uncertainty depends on model structure constraints using different norms of the likelihood function and the model constraints, and study the added benefits of joint inversion of EM and electrical resistivity tomography (ERT) data. Our results demonstrate that model structure constraints are necessary to stabilize the MCMC inversion results of a highly discretized model. These constraints decrease model parameter uncertainty and facilitate model interpretation. A drawback is that these constraints may lead to posterior distributions that do not fully include the true underlying model, because some of its features exhibit a low sensitivity to the EM data, and hence are difficult to resolve. This problem can be partly mitigated if the plane-wave EM data is augmented with ERT observations. The hierarchical Bayesian inverse formulation introduced and used herein is able to successfully recover the probabilistic properties of the measurement data errors and a model regularization weight. Application of the proposed inversion methodology to field data from an aquifer demonstrates that the posterior mean model realization is very similar to that derived from a deterministic inversion with similar model constraints.