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.

The ghost of social environments past: dominance relationships include current interactions and experience carried over from previous groups.

Dominance hierarchies pervade animal societies. Within a static social environment, in which group size and composition are unchanged, an individual's hierarchy rank results from intrinsic (e.g. body size) and extrinsic (e.g. previous experiences) factors. Little is known, however, about how dominance relationships are formed and maintained when group size and composition are dynamic. Using a fusion-fission protocol, we fused groups of previously isolated shore crabs (Carcinus maenas) into larger groups, and then restored groups to their original size and composition. Pre-fusion hierarchies formed independently of individuals' sizes, and were maintained within a static group via winner/loser effects. Post-fusion hierarchies differed from pre-fusion ones; losing fights during fusion led to a decline in an individual's rank between pre- and post-fusion conditions, while spending time being aggressive during fusion led to an improvement in rank. In post-fusion tanks, larger individuals achieved better ranks than smaller individuals. In conclusion, dominance hierarchies in crabs represent a complex combination of intrinsic and extrinsic factors, in which experiences from previous groups can carry over to affect current competitive interactions.

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.

The combination of social and personal contexts affects dominance hierarchy development in shore crabs, Carcinus maenas

Many animals that live in groups maintain competitive relationships, yet avoid continual fighting, by forming dominance hierarchies. We compare predictions of stochastic, individual-based models with empirical experimental evidence using shore crabs to test competing hypotheses regarding hierarchy development. The models test (1) what information individuals use when deciding to fight or retreat, (2) how past experience affects current resource-holding potential, and (3) how individuals deal with changes to the social environment. First, we conclude that crabs assess only their own state and not their opponent's when deciding to fight or retreat. Second, willingness to enter, and performance in, aggressive contests are influenced by previous contest outcomes. Winning increases the likelihood of both fighting and winning future interactions, while losing has the opposite effect. Third, when groups with established dominance hierarchies dissolve and new groups form, individuals reassess their ranks, showing no memory of previous rank or group affiliation. With every change in group composition, individuals fight for their new ranks. This iterative process carries over as groups dissolve and form, which has important implications for the relationship between ability and hierarchy rank. We conclude that dominance hierarchies emerge through an interaction of individual and social factors, and discuss these findings in terms of an underlying mechanism. Overall, our results are consistent with crabs using a cumulative assessment strategy iterated across changes in group composition, in which aggression is constrained by an absolute threshold in energy spent and damage received while fighting.

Mate choice evolution, dominance effects, and the maintenance of genetic variation.

Female mate choice influences the maintenance of genetic variation by altering the mating success of males with different genotypes. The evolution of preferences themselves, on the other hand, depends on genetic variation present in the population. Few models have tracked this feedback between a choice gene and its effects on genetic variation, in particular when genes that determine offspring viability and attractiveness have dominance effects. Here we build a population genetic model that allows comparing the evolution of various choice rules in a single framework. We first consider preferences for good genes and show that focused preferences for homozygotes evolve more easily than broad preferences, which allow heterozygous males high mating success too. This occurs despite better maintenance of genetic diversity in the latter scenario, and we discuss why empirical findings of superior mating success of heterozygous males consequently do not immediately lead to a better understanding of the lek paradox. Our results thus suggest that the mechanisms that help maintain genetic diversity also have a flipside of making female choice an inaccurate means of producing the desired kind of offspring. We then consider preferences for heterozygosity per se, and show that these evolve only under very special conditions. Choice for compatible genotypes can evolve but its selective advantage diminishes quickly due to frequency-dependent selection. Finally, we show that our model reproduces earlier results on selfing, when the female choice strategy produces assortative mating. Overall, our model indicates that various forms of heterozygote-favouring (or variable) female choice pose a problem for the theory of sexual ornamentation based on indirect benefits, rather than a solution.

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.

Hierarchical analyses of genetic differentiation in a hybrid zone of Sorex araneus (Insectivora : Soricidae)

Microsatellites are used to unravel the fine-scale genetic structure of a hybrid zone between chromosome races Valais and Cordon of the common shrew (Sorex araneus) located in the French Alps. A total of 269 individuals collected between 1992 and 1995 was typed for seven microsatellite loci. A modified version of the classical multiple correspondence analysis is carried out. This analysis clearly shows the dichotomy between the two races. Several approaches are used to study genetic structuring. Gene flow is clearly reduced between these chromosome races and is estimated at one migrant every two generations using X-statistics and one migrant per generation using F-statistics. Hierarchical F- and R-statistics are compared and their efficiency to detect inter- and intraracial patterns of divergence is discussed. Within-race genetic structuring is significant, but remains weak. F-ST displays similar values on both sides of the hybrid zone, although no environmental barriers are found on the Cordon side, whereas the Valais side is divided by several mountain rivers. We introduce the exact G-test to microsatellite data which proved to be a powerful test to detect genetic differentiation within as well as among races. The genetic background of karyotypic hybrids was compared with the genetic background of pure parental forms using a CRT-MCA. Our results indicate that, without knowledge of the karyotypes, we would not have been able to distinguish these hybrids from karyotypically pure samples.

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.

Hierarchical categorisation of tags for delicious

In the scenario of social bookmarking, a user browsing the Web bookmarks web pages and assigns free-text labels (i.e., tags) to them according to their personal preferences. In this technical report, we approach one of the practical aspects when it comes to represent users' interests from their tagging activity, namely the categorization of tags into high-level categories of interest. The reason is that the representation of user profiles on the basis of the myriad of tags available on the Web is certainly unfeasible from various practical perspectives; mainly concerning the unavailability of data to reliably, accurately measure interests across such fine-grained categorisation, and, should the data be available, its overwhelming computational intractability. Motivated by this, our study presents the results of a categorization process whereby a collection of tags posted at Delicious #http://delicious.com# are classified into 200 subcategories of interest.

Hierarchical management of carbon sources is regulated similarly by the CbrA/B systems in Pseudomonas aeruginosa and Pseudomonas putida.

The CbrA/B system in pseudomonads is involved in the utilization of carbon sources and carbon catabolite repression (CCR) through the activation of the small RNAs crcZ in Pseudomonas aeruginosa, and crcZ and crcY in Pseudomonas putida. Interestingly, previous works reported that the CbrA/B system activity in P. aeruginosa PAO1 and P. putida KT2442 responded differently to the presence of different carbon sources, thus raising the question of the exact nature of the signal(s) detected by CbrA. Here, we demonstrated that the CbrA/B/CrcZ(Y) signal transduction pathway is similarly activated in the two Pseudomonas species. We show that the CbrA sensor kinase is fully interchangeable between the two species and, moreover, responds similarly to the presence of different carbon sources. In addition, a metabolomics analysis supported the hypothesis that CCR responds to the internal energy status of the cell, as the internal carbon/nitrogen ratio seems to determine CCR and non-CCR conditions. The strong difference found in the 2-oxoglutarate/glutamine ratio between CCR and non-CCR conditions points to the close relationship between carbon and nitrogen availability, or the relationship between the CbrA/B and NtrB/C systems, suggesting that both regulatory systems sense the same sort or interrelated signal.

Qualitative and Hierarchical Analysis of Protective Factors against Illicit Use of Doping Substances in Athletes Calling a National Anti-Doping Phone-Help Service

Evidence of a sport-specific hierarchy of protective factors against doping would thus be a powerful aid in adapting information and prevention campaigns to target the characteristics of specific athlete groups, and especially those athletes most vulnerable for doping control. The contents of phone calls to a free and anonymous national anti-doping service called 'ecoute dopage' were analysed (192 bodybuilders, 124 cyclists and 44 footballers). The results showed that the protective factors that emerged from analysis could be categorised into two groups. The first comprised 'Health concerns', 'Respect for the law' and 'Doping controls from the environment' and the second comprised 'Doubts about the effectiveness of illicit products, 'Thinking skills' and 'Doubts about doctors'. The ranking of the factors for the cyclists differed from that of the other athletes. The ordering of factors was 1) respect for the law, 2) doping controls from the environment, 3) health concerns 4) doubts about doctors, and 5) doubts about the effectiveness illicit products. The results are analysed in terms of the ranking in each athlete group and the consequences on the athletes' experience and relationship to doping. Specific prevention campaigns are proposed to limit doping behaviour in general and for each sport.

How geographic distance and depth drive ecological variability and isolation of demersal fish communities in an archipelago system (Cape Verde, Eastern Atlantic Ocean)

Cape Verde is a tropical oceanic ecosystem, highly fragmented and dispersed, with islands physically isolated by distance and depth. To understand how isolation affects the ecological variability in this archipelago, we conducted a research project on the community structure of the 18 commercially most important demersal fishes. An index of ecological distance based on species relative dominance (Di) is developed from Catch Per Unit Effort, derived from an extensive database of artisanal fisheries. Two ecological measures of distance between islands are calculated: at the species level, DDi, and at the community level, DD (sum of DDi). A physical isolation factor (Idb) combining distance (d) and bathymetry (b) is proposed. Covariance analysis shows that isolation factor is positively correlated with both DDi and DD, suggesting that Idb can be considered as an ecological isolation factor. The effect of Idb varies with season and species. This effect is stronger in summer (May to November), than in winter (December to April), which appears to be more unstable. Species react differently to Idb, independently of season. A principal component analysis on the monthly (DDi) for the 12 islands and the 18 species, complemented by an agglomerative hierarchical clustering, shows a geographic pattern of island organization, according to Idb. Results indicate that the ecological structure of demersal fish communities of Cape Verde archipelago, both in time and space, can be explained by a geographic isolation factor. The analytical approach used here is promising and could be tested in other archipelago systems.

Emotion and reason in everyday risk perception

Although research has documented the importance of emotion in risk perception, little is knownabout its prevalence in everyday life. Using the Experience Sampling Method, 94 part-timestudents were prompted at random via cellular telephones to report on mood state and threeemotions and to assess risk on thirty occasions during their working hours. The emotions valence, arousal, and dominance were measured using self-assessment manikins (Bradley &Lang, 1994). Hierarchical linear models (HLM) revealed that mood state and emotions explainedsignificant variance in risk perception. In addition, valence and arousal accounted for varianceover and above reason (measured by severity and possibility of risks). Six risks were reassessedin a post-experimental session and found to be lower than their real-time counterparts.The study demonstrates the feasibility and value of collecting representative samples of data withsimple technology. Evidence for the statistical consistency of the HLM estimates is provided inan Appendix.

Stochastic dominance and absolute risk aversion

In this paper we proose the infimum of the Arrow-Pratt index of absoluterisk aversion as a measure of global risk aversion of a utility function.We then show that, for any given arbitrary pair of distributions, thereexists a threshold level of global risk aversion such that all increasingconcave utility functions with at least as much global risk aversion wouldrank the two distributions in the same way. Furthermore, this thresholdlevel is sharp in the sense that, for any lower level of global riskaversion, we can find two utility functions in this class yielding oppositepreference relations for the two distributions.