Global habitat suitability models of terrestrial mammals.

Detailed large-scale information on mammal distribution has often been lacking, hindering conservation efforts. We used the information from the 2009 IUCN Red List of Threatened Species as a baseline for developing habitat suitability models for 5027 out of 5330 known terrestrial mammal species, based on their habitat relationships. We focused on the following environmental variables: land cover, elevation and hydrological features. Models were developed at 300 m resolution and limited to within species' known geographical ranges. A subset of the models was validated using points of known species occurrence. We conducted a global, fine-scale analysis of patterns of species richness. The richness of mammal species estimated by the overlap of their suitable habitat is on average one-third less than that estimated by the overlap of their geographical ranges. The highest absolute difference is found in tropical and subtropical regions in South America, Africa and Southeast Asia that are not covered by dense forest. The proportion of suitable habitat within mammal geographical ranges correlates with the IUCN Red List category to which they have been assigned, decreasing monotonically from Least Concern to Endangered. These results demonstrate the importance of fine-resolution distribution data for the development of global conservation strategies for mammals.

Analytical interference of 4-hydroxy-3-methoxymethamphetamine with the measurement of plasma free normetanephrine by ultra-high pressure liquid chromatography-tandem mass spectrometry.

OBJECTIVES: The diagnosis of pheochromocytoma relies on the measurement of plasma free metanephrines assay whose reliability has been considerably improved by ultra-high pressure liquid chromatography tandem mass spectrometry (UHPLC-MS/MS). Here we report an analytical interference occurring between 4-hydroxy-3-methoxymethamphetamine (HMMA), a metabolite of 3,4-methylenedioxymethamphetamine (MDMA, "Ecstasy"), and normetanephrine (NMN) since they share a common pharmacophore resulting in the same product ion after fragmentation. DESIGN AND METHODS: Synthetic HMMA was spiked into plasma samples containing various concentrations of NMN and the intensity of the interference was determined by UPLC-MS/MS before and after improvement of the analytical method. RESULTS: Using a careful adjustment of chromatographic conditions including the change of the UPLC analytical column, we were able to distinguish both compounds. HMMA interference for NMN determination should be seriously considered since MDMA activates the sympathetic nervous system and if confounded with NMN may lead to false-positive tests when performing a differential diagnostic of pheochromocytoma.

Thymic stromal lymphopoietin plays divergent roles in murine models of atopic and nonatopic airway inflammation.

BACKGROUND: Thymic stromal lymphopoietin (TSLP) is a cytokine primarily produced by epithelial cells, which has been shown to be a potent inducer of T-helper 2 (Th2)-type responses. However, TSLP has pleiotropic effects upon immune cells, and although extensively studied in the context of atopic asthma, its relevance as a therapeutic target and its role in the pathogenesis of nonatopic asthma remains unknown. We sought to investigate the role of TSLP in atopic, nonatopic and viral-induced exacerbations of pulmonary inflammation. METHODS: Using stringently defined murine models of atopic, nonatopic and virally exacerbated forms of pulmonary inflammation, we compared inflammatory responses of C57BL/6 wild-type (WT) and TSLP receptor-deficient (TSLPR KO) mice. RESULTS: Thymic stromal lymphopoietin receptor (TSLPR) signaling was crucial for the development of atopic asthma. Specifically, TSLPR signaling to lung recruited CD4+ T cells enhanced eosinophilia, goblet cell hyperplasia, and overall inflammation within the airways. In contrast, the absence of TSLPR signaling was associated with strikingly exaggerated pulmonary neutrophilic inflammation in a nonatopic model of airway inflammation. The inflammation was associated with excessive levels of interleukin (IL)-17A in the lungs, indicating that TSLP negatively regulates IL-17A. In addition, in a model of influenza-induced exacerbation of atopic airway inflammation, the absence of TSLPR signaling also led to exaggerated neutrophilic inflammation. CONCLUSION: Thymic stromal lymphopoietin plays divergent roles in the pathogenesis of atopic and nonatopic asthma phenotypes by either enhancing Th2 responses or curtailing T-helper 17 responses. These findings raise important caveats for the design of therapeutic interventions targeting TSLP in asthma.

Dynamic models of energy allocation and investment

In dynamic models of energy allocation, assimilated energy is allocated to reproduction, somatic growth, maintenance or storage, and the allocation pattern can change with age. The expected evolutionary outcome is an optimal allocation pattern, but this depends on the environment experienced during the evolutionary process and on the fitness costs and benefits incurred by allocating resources in different ways. Here we review existing treatments which encompass some of the possibilities as regards constant or variable environments and their predictability or unpredictability, and the ways in which production rates and mortality rates depend on body size and composition and age and on the pattern of energy allocation. The optimal policy is to allocate resources where selection pressures are highest, and simultaneous allocation to several body subsystems and reproduction can be optimal if these pressures are equal. This may explain balanced growth commonly observed during ontogeny. Growth ceases at maturity in many models; factors favouring growth after maturity include non-linear trade-offs, variable season length, and production and mortality rates both increasing (or decreasing) functions of body size. We cannot yet say whether these are sufficient to account for the many known cases of growth after maturity and not all reasonable models have yet been explored. Factors favouring storage are also reviewed.

Introduction of snow and geomorphic disturbance variables into predictive models of alpine plant distribution in the Western Swiss Alps

Indirect topographic variables have been used successfully as surrogates for disturbance processes in plant species distribution models (SDM) in mountain environments. However, no SDM studies have directly tested the performance of disturbance variables. In this study, we developed two disturbance variables: a geomorphic index (GEO) and an index of snow redistribution by wind (SNOW). These were developed in order to assess how they improved both the fit and predictive power of presenceabsence SDM based on commonly used topoclimatic (TC) variables for 91 plants in the Western Swiss Alps. The individual contribution of the disturbance variables was compared to TC variables. Maps of models were prepared to spatially test the effect of disturbance variables. On average, disturbance variables significantly improved the fit but not the predictive power of the TC models and their individual contribution was weak (5.6% for GEO and 3.3% for SNOW). However their maximum individual contribution was important (24.7% and 20.7%). Finally, maps including disturbance variables (i) were significantly divergent from TC models in terms of predicted suitable surfaces and connectivity between potential habitats, and (ii) were interpreted as more ecologically relevant. Disturbance variables did not improve the transferability of models at the local scale in a complex mountain system, and the performance and contribution of these variables were highly species-specific. However, improved spatial projections and change in connectivity are important issues when preparing projections under climate change because the future range size of the species will determine the sensitivity to changing conditions.

Establishment and characterization of models of chemotherapy resistance in colorectal cancer: Towards a predictive signature of chemoresistance.

Current standard treatments for metastatic colorectal cancer (CRC) are based on combination regimens with one of the two chemotherapeutic drugs, irinotecan or oxaliplatin. However, drug resistance frequently limits the clinical efficacy of these therapies. In order to gain new insights into mechanisms associated with chemoresistance, and departing from three distinct CRC cell models, we generated a panel of human colorectal cancer cell lines with acquired resistance to either oxaliplatin or irinotecan. We characterized the resistant cell line variants with regards to their drug resistance profile and transcriptome, and matched our results with datasets generated from relevant clinical material to derive putative resistance biomarkers. We found that the chemoresistant cell line variants had distinctive irinotecan- or oxaliplatin-specific resistance profiles, with non-reciprocal cross-resistance. Furthermore, we could identify several new, as well as some previously described, drug resistance-associated genes for each resistant cell line variant. Each chemoresistant cell line variant acquired a unique set of changes that may represent distinct functional subtypes of chemotherapy resistance. In addition, and given the potential implications for selection of subsequent treatment, we also performed an exploratory analysis, in relevant patient cohorts, of the predictive value of each of the specific genes identified in our cellular models.

Differential associations of personal and general just-world beliefs with the Five-Factor and HEXACO models of personality.

Recent literature evidences differential associations of personal and general just-world beliefs with constructs in the interpersonal domain. In line with this research, we examine the respective relationships of each just-world belief with the Five-Factor and the HEXACO models of personality in one representative sample of the working population of Switzerland and one sample of the general US population, respectively. One suppressor effect was observed in both samples: Neuroticism and emotionality was positively associated with general just-world belief, but only after controlling for personal just-world belief. In addition, agreeableness was positively and honesty-humility negatively associated with general just-world belief but unrelated to personal just-world belief. Conscientiousness was consistently unrelated to any of the just-world belief and extraversion and openness to experience revealed unstable coefficients across studies. We discuss these points in light of just-world theory and their implications for future research taking both dimensions into account.

Biotic interactions boost spatial models of species richness

Biotic interactions are known to affect the composition of species assemblages via several mechanisms, such as competition and facilitation. However, most spatial models of species richness do not explicitly consider inter-specific interactions. Here, we test whether incorporating biotic interactions into high-resolution models alters predictions of species richness as hypothesised. We included key biotic variables (cover of three dominant arctic-alpine plant species) into two methodologically divergent species richness modelling frameworks - stacked species distribution models (SSDM) and macroecological models (MEM) - for three ecologically and evolutionary distinct taxonomic groups (vascular plants, bryophytes and lichens). Predictions from models including biotic interactions were compared to the predictions of models based on climatic and abiotic data only. Including plant-plant interactions consistently and significantly lowered bias in species richness predictions and increased predictive power for independent evaluation data when compared to the conventional climatic and abiotic data based models. Improvements in predictions were constant irrespective of the modelling framework or taxonomic group used. The global biodiversity crisis necessitates accurate predictions of how changes in biotic and abiotic conditions will potentially affect species richness patterns. Here, we demonstrate that models of the spatial distribution of species richness can be improved by incorporating biotic interactions, and thus that these key predictor factors must be accounted for in biodiversity forecasts