A cross-cultural study of the higher-order structures underlying personality disorders in French-speaking Africa and Switzerland

Most studies about the higher-order dimensions to be considered in order to parsimoniously describe Personality Disorders (PDs) have identified between two and four factors but there is still no consensus about their exact number. In this context, the cultural stability of these structures might be a criterion to be considered. The aim of this study was to identify stable higher-order structures of PD traits in a French-speaking African and Swiss sample (N = 2,711). All subject completed the IPDE screening questionnaire. Using Everett's criterion and conducting a series of principal component analyses, a cross-culturally stable two- and four-factor structure were identified, associated with a total congruence coefficient of respectively .98 and .94 after Procrustes rotation. Moreover, these two structures were also highly replicable across the four African regions considered, North Africa, West Africa, Central Africa, and Mauritius, with a mean total congruence coefficient of respectively .97 and .87. The four-factor structure presented the advantage of being similar to Livesely's four components and of describing the ten PDs more accurately.

Exposure models in Switzerland: an overview of the present situation

A survey was undertaken among Swiss occupational health and safety specialists in 2004 to identify uses, difficulties, and possible developments of exposure models. Occupational hygienists (121), occupational physicians (169), and safety specialists (95) were surveyed with an in depth questionnaire. Results obtained indicate that models are not used very much in practice in Switzerland and are reserved to research groups focusing on specific topics. However, various determinants of exposure are often considered important by professionals (emission rate, work activity), and in some cases recorded and used (room parameters, operator activity). These parameters cannot be directly included in present models. Nevertheless, more than half of the occupational hygienists think that it is important to develop quantitative exposure models. Looking at research institutions, there is, however, a big interest in the use of models to solve problems which are difficult to address with direct measurements; i. e. retrospective exposure assessment for specific clinical cases and prospective evaluation for new situations or estimation of the effect of selected parameters. In a recent study about cases of acutepulmonary toxicity following water proofing spray exposure, exposure models have been used to reconstruct exposure of a group of patients. Finally, in the context of exposure prediction, it is also important to report about a measurement database existing in Switzerland since 1991. [Authors]

Validation of Logistic Regression Models for Landslide Susceptibility Maps

A wide range of numerical models and tools have been developed over the last decades to support the decision making process in environmental applications, ranging from physical models to a variety of statistically-based methods. In this study, a landslide susceptibility map of a part of Three Gorges Reservoir region of China was produced, employing binary logistic regression analyses. The available information includes the digital elevation model of the region, geological map and different GIS layers including land cover data obtained from satellite imagery. The landslides were observed and documented during the field studies. The validation analysis is exploited to investigate the quality of mapping.

Identification of a new murine tumor necrosis factor receptor locus that contains two novel murine receptors for tumor necrosis factor-related apoptosis-inducing ligand (TRAIL).

Tumor necrosis factor (TNF) ligand and receptor superfamily members play critical roles in diverse developmental and pathological settings. In search for novel TNF superfamily members, we identified a murine chromosomal locus that contains three new TNF receptor-related genes. Sequence alignments suggest that the ligand binding regions of these murine TNF receptor homologues, mTNFRH1, -2 and -3, are most homologous to those of the tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) receptors. By using a number of in vitro ligand-receptor binding assays, we demonstrate that mTNFRH1 and -2, but not mTNFRH3, bind murine TRAIL, suggesting that they are indeed TRAIL receptors. This notion is further supported by our demonstration that both mTNFRH1:Fc and mTNFRH2:Fc fusion proteins inhibited mTRAIL-induced apoptosis of Jurkat cells. Unlike the only other known murine TRAIL receptor mTRAILR2, however, neither mTNFRH2 nor mTNFRH3 has a cytoplasmic region containing the well characterized death domain motif. Coupled with our observation that overexpression of mTNFRH1 and -2 in 293T cells neither induces apoptosis nor triggers NFkappaB activation, we propose that the mTnfrh1 and mTnfrh2 genes encode the first described murine decoy receptors for TRAIL, and we renamed them mDcTrailr1 and -r2, respectively. Interestingly, the overall sequence structures of mDcTRAILR1 and -R2 are quite distinct from those of the known human decoy TRAIL receptors, suggesting that the presence of TRAIL decoy receptors represents a more recent evolutionary event.

Generalized linear and generalized additive models in studies of species distributions: setting the scene

An important statistical development of the last 30 years has been the advance in regression analysis provided by generalized linear models (GLMs) and generalized additive models (GAMs). Here we introduce a series of papers prepared within the framework of an international workshop entitled: Advances in GLMs/GAMs modeling: from species distribution to environmental management, held in Riederalp, Switzerland, 6-11 August 2001.We first discuss some general uses of statistical models in ecology, as well as provide a short review of several key examples of the use of GLMs and GAMs in ecological modeling efforts. We next present an overview of GLMs and GAMs, and discuss some of their related statistics used for predictor selection, model diagnostics, and evaluation. Included is a discussion of several new approaches applicable to GLMs and GAMs, such as ridge regression, an alternative to stepwise selection of predictors, and methods for the identification of interactions by a combined use of regression trees and several other approaches. We close with an overview of the papers and how we feel they advance our understanding of their application to ecological modeling.

Testing demographic models of effective population size.

Due to practical difficulties in obtaining direct genetic estimates of effective sizes, conservation biologists have to rely on so-called 'demographic models' which combine life-history and mating-system parameters with F-statistics in order to produce indirect estimates of effective sizes. However, for the same practical reasons that prevent direct genetic estimates, the accuracy of demographic models is difficult to evaluate. Here we use individual-based, genetically explicit computer simulations in order to investigate the accuracy of two such demographic models aimed at investigating the hierarchical structure of populations. We show that, by and large, these models provide good estimates under a wide range of mating systems and dispersal patterns. However, one of the models should be avoided whenever the focal species' breeding system approaches monogamy with no sex bias in dispersal or when a substructure within social groups is suspected because effective sizes may then be strongly overestimated. The timing during the life cycle at which F-statistics are evaluated is also of crucial importance and attention should be paid to it when designing field sampling since different demographic models assume different timings. Our study shows that individual-based, genetically explicit models provide a promising way of evaluating the accuracy of demographic models of effective size and delineate their field of applicability.

Tectonics of the Simplon massif and Lepontine gneiss dome: deformation structures due to collision between the underthrusting European plate and the Adriatic indenter

this study presents a review of published geological data, combined with original observations on the tectonics of the simplon massif and the Lepontine gneiss dome in the Western Alps. New observations concern the geometry of the Oligocene Vanzone back fold, formed under amphibolite facies conditions, and of its root between Domodossola and Locarno, which is cut at an acute angle by the Miocene, epi- to anchizonal, dextral centovalli strike-slip fault. the structures of the simplon massif result from collision over 50 Ma between two plate boundaries with a different geometry: the underthrusted European plate and the Adriatic indenter. Detailed mapping and analysis of a complex structural interference pattern, combined with observations on the metamorphic grade of the superimposed structures and radiometric data, allow a kinematic model to be developed for this zone of oblique continental collision. the following main Alpine tectonic phases and structures may be distinguished: 1. NW-directed nappe emplacement, starting in the Early Eocene (similar to 50 Ma); 2. W, SW and S- verging transverse folds; 3. transpressional movements on the dextral simplon ductile shear zone since similar to 32 Ma; 4. formation of the Bergell - Vanzone backfolds and of the southern steep belt during the Oligocene, emplacement of the mantle derived 31 - 29 Ma Bergell and Biella granodiorites and porphyritic andesites as well as intrusions of 29-25 Ma crustal aplites and pegmatites; 5. formation of the dextral discrete Rhone-Simplon line and the centovalli line during the Miocene, accompanied by the pull-apart development of the Lepontine gneiss dome - Dent blanche (Valpelline) depression. It is suggested that movements of shortening in fan shaped NW, W and sW directions accompanied the more regular NW- to WNW-directed displacement of the Adriatic indenter during continental collision.

Evaluation of uptake and transport of cationic and anionic ultrasmall iron oxide nanoparticles by human colon cells

Nanoparticles (NPs) are in clinical use or under development for therapeutic imaging and drug delivery. However, relatively little information exists concerning the uptake and transport of NPs across human colon cell layers, or their potential to invade three-dimensional models of human colon cells that better mimic the tissue structures of normal and tumoral colon. In order to gain such information, the interactions of biocompatible ultrasmall superparamagnetic iron oxide nanoparticles (USPIO NPs) (iron oxide core 9-10 nm) coated with either cationic polyvinylamine (aminoPVA) or anionic oleic acid with human HT-29 and Caco-2 colon cells was determined. The uptake of the cationic USPIO NPs was much higher than the uptake of the anionic USPIO NPs. The intracellular localization of aminoPVA USPIO NPs was confirmed in HT-29 cells by transmission electron microscopy that detected the iron oxide core. AminoPVA USPIO NPs invaded three-dimensional spheroids of both HT-29 and Caco-2 cells, whereas oleic acid-coated USPIO NPs could only invade Caco-2 spheroids. Neither cationic aminoPVA USPIO NPs nor anionic oleic acid-coated USPIO NPs were transported at detectable levels across the tight CacoReady? intestinal barrier model or the more permeable mucus-secreting CacoGoblet? model.

Defining new criteria for selection of cell-based intestinal models using publicly available databases.

BACKGROUND: The criteria for choosing relevant cell lines among a vast panel of available intestinal-derived lines exhibiting a wide range of functional properties are still ill-defined. The objective of this study was, therefore, to establish objective criteria for choosing relevant cell lines to assess their appropriateness as tumor models as well as for drug absorption studies. RESULTS: We made use of publicly available expression signatures and cell based functional assays to delineate differences between various intestinal colon carcinoma cell lines and normal intestinal epithelium. We have compared a panel of intestinal cell lines with patient-derived normal and tumor epithelium and classified them according to traits relating to oncogenic pathway activity, epithelial-mesenchymal transition (EMT) and stemness, migratory properties, proliferative activity, transporter expression profiles and chemosensitivity. For example, SW480 represent an EMT-high, migratory phenotype and scored highest in terms of signatures associated to worse overall survival and higher risk of recurrence based on patient derived databases. On the other hand, differentiated HT29 and T84 cells showed gene expression patterns closest to tumor bulk derived cells. Regarding drug absorption, we confirmed that differentiated Caco-2 cells are the model of choice for active uptake studies in the small intestine. Regarding chemosensitivity we were unable to confirm a recently proposed association of chemo-resistance with EMT traits. However, a novel signature was identified through mining of NCI60 GI50 values that allowed to rank the panel of intestinal cell lines according to their drug responsiveness to commonly used chemotherapeutics. CONCLUSIONS: This study presents a straightforward strategy to exploit publicly available gene expression data to guide the choice of cell-based models. While this approach does not overcome the major limitations of such models, introducing a rank order of selected features may allow selecting model cell lines that are more adapted and pertinent to the addressed biological question.

Evaluation of uptake and transport of cationic and anionic ultrasmall iron oxide nanoparticles by human colon cells.

Nanoparticles (NPs) are in clinical use or under development for therapeutic imaging and drug delivery. However, relatively little information exists concerning the uptake and transport of NPs across human colon cell layers, or their potential to invade three-dimensional models of human colon cells that better mimic the tissue structures of normal and tumoral colon. In order to gain such information, the interactions of biocompatible ultrasmall superparamagnetic iron oxide nanoparticles (USPIO NPs) (iron oxide core 9-10 nm) coated with either cationic polyvinylamine (aminoPVA) or anionic oleic acid with human HT-29 and Caco-2 colon cells was determined. The uptake of the cationic USPIO NPs was much higher than the uptake of the anionic USPIO NPs. The intracellular localization of aminoPVA USPIO NPs was confirmed in HT-29 cells by transmission electron microscopy that detected the iron oxide core. AminoPVA USPIO NPs invaded three-dimensional spheroids of both HT-29 and Caco-2 cells, whereas oleic acid-coated USPIO NPs could only invade Caco-2 spheroids. Neither cationic aminoPVA USPIO NPs nor anionic oleic acid-coated USPIO NPs were transported at detectable levels across the tight CacoReady? intestinal barrier model or the more permeable mucus-secreting CacoGoblet? model.

Do habitat suitability models reliably predict the recovery areas of threatened species?

1. Identifying those areas suitable for recolonization by threatened species is essential to support efficient conservation policies. Habitat suitability models (HSM) predict species' potential distributions, but the quality of their predictions should be carefully assessed when the species-environment equilibrium assumption is violated.2. We studied the Eurasian otter Lutra lutra, whose numbers are recovering in southern Italy. To produce widely applicable results, we chose standard HSM procedures and looked for the models' capacities in predicting the suitability of a recolonization area. We used two fieldwork datasets: presence-only data, used in the Ecological Niche Factor Analyses (ENFA), and presence-absence data, used in a Generalized Linear Model (GLM). In addition to cross-validation, we independently evaluated the models with data from a recolonization event, providing presences on a previously unoccupied river.3. Three of the models successfully predicted the suitability of the recolonization area, but the GLM built with data before the recolonization disagreed with these predictions, missing the recolonized river's suitability and badly describing the otter's niche. Our results highlighted three points of relevance to modelling practices: (1) absences may prevent the models from correctly identifying areas suitable for a species spread; (2) the selection of variables may lead to randomness in the predictions; and (3) the Area Under Curve (AUC), a commonly used validation index, was not well suited to the evaluation of model quality, whereas the Boyce Index (CBI), based on presence data only, better highlighted the models' fit to the recolonization observations.4. For species with unstable spatial distributions, presence-only models may work better than presence-absence methods in making reliable predictions of suitable areas for expansion. An iterative modelling process, using new occurrences from each step of the species spread, may also help in progressively reducing errors.5. Synthesis and applications. Conservation plans depend on reliable models of the species' suitable habitats. In non-equilibrium situations, such as the case for threatened or invasive species, models could be affected negatively by the inclusion of absence data when predicting the areas of potential expansion. Presence-only methods will here provide a better basis for productive conservation management practices.

A probabilistic approach to niche-based community models for spatial forecasts of assemblage properties and their uncertainties

Aim Conservation strategies are in need of predictions that capture spatial community composition and structure. Currently, the methods used to generate these predictions generally focus on deterministic processes and omit important stochastic processes and other unexplained variation in model outputs. Here we test a novel approach of community models that accounts for this variation and determine how well it reproduces observed properties of alpine butterfly communities. Location The western Swiss Alps. Methods We propose a new approach to process probabilistic predictions derived from stacked species distribution models (S-SDMs) in order to predict and assess the uncertainty in the predictions of community properties. We test the utility of our novel approach against a traditional threshold-based approach. We used mountain butterfly communities spanning a large elevation gradient as a case study and evaluated the ability of our approach to model species richness and phylogenetic diversity of communities. Results S-SDMs reproduced the observed decrease in phylogenetic diversity and species richness with elevation, syndromes of environmental filtering. The prediction accuracy of community properties vary along environmental gradient: variability in predictions of species richness was higher at low elevation, while it was lower for phylogenetic diversity. Our approach allowed mapping the variability in species richness and phylogenetic diversity projections. Main conclusion Using our probabilistic approach to process species distribution models outputs to reconstruct communities furnishes an improved picture of the range of possible assemblage realisations under similar environmental conditions given stochastic processes and help inform manager of the uncertainty in the modelling results

Classification of urban multi-angular image sequences by aligning their manifolds

When dealing with multi-angular image sequences, problems of reflectance changes due either to illumination and acquisition geometry, or to interactions with the atmosphere, naturally arise. These phenomena interplay with the scene and lead to a modification of the measured radiance: for example, according to the angle of acquisition, tall objects may be seen from top or from the side and different light scatterings may affect the surfaces. This results in shifts in the acquired radiance, that make the problem of multi-angular classification harder and might lead to catastrophic results, since surfaces with the same reflectance return significantly different signals. In this paper, rather than performing atmospheric or bi-directional reflection distribution function (BRDF) correction, a non-linear manifold learning approach is used to align data structures. This method maximizes the similarity between the different acquisitions by deforming their manifold, thus enhancing the transferability of classification models among the images of the sequence.

Advanced geostatistical and machine-learning models for spatial data analysis of radioactively contaminated regions

Radioactive soil-contamination mapping and risk assessment is a vital issue for decision makers. Traditional approaches for mapping the spatial concentration of radionuclides employ various regression-based models, which usually provide a single-value prediction realization accompanied (in some cases) by estimation error. Such approaches do not provide the capability for rigorous uncertainty quantification or probabilistic mapping. Machine learning is a recent and fast-developing approach based on learning patterns and information from data. Artificial neural networks for prediction mapping have been especially powerful in combination with spatial statistics. A data-driven approach provides the opportunity to integrate additional relevant information about spatial phenomena into a prediction model for more accurate spatial estimates and associated uncertainty. Machine-learning algorithms can also be used for a wider spectrum of problems than before: classification, probability density estimation, and so forth. Stochastic simulations are used to model spatial variability and uncertainty. Unlike regression models, they provide multiple realizations of a particular spatial pattern that allow uncertainty and risk quantification. This paper reviews the most recent methods of spatial data analysis, prediction, and risk mapping, based on machine learning and stochastic simulations in comparison with more traditional regression models. The radioactive fallout from the Chernobyl Nuclear Power Plant accident is used to illustrate the application of the models for prediction and classification problems. This fallout is a unique case study that provides the challenging task of analyzing huge amounts of data ('hard' direct measurements, as well as supplementary information and expert estimates) and solving particular decision-oriented problems.

Case studies and mathematical models of ecological speciation. 1. Cichlids in a crater lake.

A recent study of a pair of sympatric species of cichlids in Lake Apoyo in Nicaragua is viewed as providing probably one of the most convincing examples of sympatric speciation to date. Here, we describe and study a stochastic, individual-based, explicit genetic model tailored for this cichlid system. Our results show that relatively rapid (<20,000 generations) colonization of a new ecological niche and (sympatric or parapatric) speciation via local adaptation and divergence in habitat and mating preferences are theoretically plausible if: (i) the number of loci underlying the traits controlling local adaptation, and habitat and mating preferences is small; (ii) the strength of selection for local adaptation is intermediate; (iii) the carrying capacity of the population is intermediate; and (iv) the effects of the loci influencing nonrandom mating are strong. We discuss patterns and timescales of ecological speciation identified by our model, and we highlight important parameters and features that need to be studied empirically to provide information that can be used to improve the biological realism and power of mathematical models of ecological speciation.