Cell surface expression of a functional rubella virus E1 glycoprotein by addition of a GPI anchor.

Rubella virus (RV) envelope glycoproteins E1 and E2 are targeted to the Golgi as heterodimers. While E2 contains a transmembrane Golgi retention signal, E1 is arrested in a pre-Golgi compartment in the absence of E2, and appears to require heterodimerization in order to reach the Golgi. Various forms of E1 with deletions in the ectodomain or lacking the cytoplasmic (CT) and transmembrane (TM) domains, as well as the 29 C-terminal amino acid residues of the ectodomain were also retained intracellularly. We therefore investigated the possibility of targetting E1 to the plasma membrane by addition of a glycosylphosphatidylinositol (GPI) anchor. We found that E1GPI was transported to the cell surface where it retained the hemadsorption activity characteristic of the wild-type E1/E2 heterodimer. Furthermore, coexpression of a mammalian GPI-specific phospholipase D (GPI-PLD) resulted in the release of E1GPI and in constitutive expression of a soluble form of E1. This study thus demonstrates that the GPI anchor has a dominant effect over the E1 pre-Golgi retention signal and that E1 is sufficient for hemadsorption.

'Next generation' biogeography: towards understanding the drivers of species diversification and persistence

The drivers of species diversification and persistence are of great interest to current biogeography, especially in those global biodiversity hotspots' harbouring most of Earth's animal and plant life. Classical multispecies biogeographical work has yielded fascinating insights into broad-scale patterns of diversification, and DNA-based intraspecific phylogeographical studies have started to complement this picture at much finer temporal and spatial scales. The advent of novel next-generation sequencing (NGS) technologies provides the opportunity to greatly scale up the numbers of individuals, populations and species sampled, potentially merging intraspecific and interspecific approaches to biogeographical inference. Here, we outline these prospects and issues by using the example of an undisputed hotspot, the Cape of southern Africa. We outline the current state of knowledge on the biogeography of species diversification within the Cape, review the literature for phylogeographical evidence of its likely drivers and mechanisms, and suggest possible ways forward based on NGS approaches. We demonstrate the potential of these methods and current bioinformatic issues with the help of restriction-site-associated DNA (RAD) sequencing data for three highly divergent species of the Restionaceae, an important plant radiation in the Cape. A thorough understanding of the mechanisms that facilitate species diversification and persistence in spatially structured, species-rich environments will require the adoption of novel genomic and bioinformatic tools in biogeographical studies.

The MIGCLIM R package - seamless integration of dispersal constraints into projections of species distribution models

The MIGCLIM R package is a function library for the open source R software that enables the implementation of species-specific dispersal constraints into projections of species distribution models under environmental change and/or landscape fragmentation scenarios. The model is based on a cellular automaton and the basic modeling unit is a cell that is inhabited or not. Model parameters include dispersal distance and kernel, long distance dispersal, barriers to dispersal, propagule production potential and habitat invasibility. The MIGCLIM R package has been designed to be highly flexible in the parameter values it accepts, and to offer good compatibility with existing species distribution modeling software. Possible applications include the projection of future species distributions under environmental change conditions and modeling the spread of invasive species.

Addition of inspiratory resistance increases the amplitude of the slow component of O2 uptake kinetics.

The contribution of respiratory muscle work to the development of the O(2) consumption (Vo(2)) slow component is a point of controversy because it has been shown that the increased ventilation in hypoxia is not associated with a concomitant increase in Vo(2) slow component. The first purpose of this study was thus to test the hypothesis of a direct relationship between respiratory muscle work and Vo(2) slow component by manipulating inspiratory resistance. Because the conditions for a Vo(2) slow component specific to respiratory muscle can be reached during intense exercise, the second purpose was to determine whether respiratory muscles behave like limb muscles during heavy exercise. Ten trained subjects performed two 8-min constant-load heavy cycling exercises with and without a threshold valve in random order. Vo(2) was measured breath by breath by using a fast gas exchange analyzer, and the Vo(2) response was modeled after removal of the cardiodynamic phase by using two monoexponential functions. As anticipated, when total work was slightly increased with loaded inspiratory resistance, slight increases in base Vo(2), the primary phase amplitude, and peak Vo(2) were noted (14.2%, P < 0.01; 3.5%, P > 0.05; and 8.3%, P < 0.01, respectively). The bootstrap method revealed small coefficients of variation for the model parameter, including the slow-component amplitude and delay (15 and 19%, respectively), indicating an accurate determination for this critical parameter. The amplitude of the Vo(2) slow component displayed a 27% increase from 8.1 +/- 3.6 to 10.3 +/- 3.4 ml. min(-1). kg(-1) (P < 0.01) with the addition of inspiratory resistance. Taken together, this increase and the lack of any differences in minute volume and ventilatory parameters between the two experimental conditions suggest the occurrence of a Vo(2) slow component specific to the respiratory muscles in loaded condition.

Is the effect of inguinal field block with 0.5 % bupivacaine on postoperative pain after hernia repair enhanced by addition of ketorolac or S(+) ketamine ?

Résumé Malgré l'apparition de nouvelles techniques chirurgicales dites « sans tension », l'antalgie post-opératoire après cure de hernie inguinale reste un défi pour les anesthésiologistes. Récemment on a suggéré que l'addition de ketamine ou d'un anti-inflammatoire non-stéroïdien (AINS) à un anesthésique local pourrait améliorer et prolonger l'analgésie postopératoire. Le but de cette étude, à laquelle ont participé 36 patients ASA I-II, était d'évaluer si la coadministration de S(+) ketamine ou de ketorolac renforcerait les effets analgésiques de la bupivacaïne après cure ambulatoire de hernie inguinale sous anesthésie générale. L'analgésie a consisté en une infiltration de la plaie associé à un bloc inguinal avec soit 30 ml de bupivacaïne 0,5 % (n=12), soit 27 ml de bupivacaïne 0,5 % + 3 ml de S(+) ketamine (75 mg) (n=12), soit 28 ml de bupivacaïne 0,5 % + 2 ml de ketorolac (60 mg) (n=12). La prise orale d'antalgique en phase postopératoire était standardisée. L'intensité des douleurs a été évaluée au moyen d'une échelle visuelle analogique (EVA), d'un score verbal d'estimation et par algométrie de pression respectivement 2, 4, 6, 24 et 48 heures après l'intervention. Les trois groupes de patients ont présenté le score de douleur évalué par EVA le plus élevé à 24 heures, score significativement différent de ceux mesurés à 6 et 48 heures (P <0.05). A part une sensation de douleurs significativement moindre (score verbal d'estimation) dans le groupe ketorolac à 24 et 48 heures et seulement à 48 heures dans le groupe ketamine, il n'y avait aucune autre différence entre les groupes pour la durée de l'étude (48 heures) en ce qui concerne les scores de douleur, les seuils de douleur à la pression ou la prise postopératoire d'antalgiques « de secours ». En conclusion, l'addition de S(+) ketamine ou de ketorolac, n'améliore que marginalement l'effet analgésique de la bupivacaïne. Ceci peut-être mis en relation avec la technique de cure de hernie « sans tension » induisant un bas niveau de douleur postopératoire. Abstract Objective: The aim of the study was to assess whether coadministration of S(±) ketamine or ketorolac would enhance or prolong local analgesic effect of bupivacaine after inguinal hernia repair. Design: Prospective double-blind randomized study evaluating pain intensity after surgery under general anesthesia. Setting: Outpatient facilities of the University Hospital of Lausanne. Patient: Thirty-six ASA I-II outpatients scheduled for elective day-case inguinal herniorraphy. Intervention: Analgesia strategy consisted of a wound infiltration and an inguinal field block either with 30 mL bupivacairie (0.5%) or with the same volume of a mixture of 27 mL bupivacaine (0.5%) + 3 mL S(+) ketamine (75 mg) or a 28 mL bupivacaine (0.5%) + 2 ML ketorolac (60 mg). Postoperative analgesic regimen was standardized. Outcome Measures: Pain intensity was assessed with a Visual Analog Seale, a verbal rating score, and by pressure algometry 2, 4, 6, 24, and 48 hours after surgery. Results: The 3 groups of patients experienced the highest Visual Analog Scale pain score at 24 hours, which was different from those at 6 and 48 hours (P < 0.05). Apart from a significantly lower pain sensation (verbal rating score) in the ketorolac group at 24 and 48 hours and only at 48 hours with ketamine, there were no other differences in pain scores, pain pressure thresholds, or rescue analgesic consumption between groups throughout the 48-hour study period. Conclusion: The addition of S (+)-ketamine or ketorolac only minimally improves the analgesic effect of bupivacaine. This may be related to the tension-free hernia repair technique associated with low postoperative pain.

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.

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.

SESAM - a new framework integrating macroecological and species distribution models for predicting spatio-temporal patterns of species assemblages

Two different approaches currently prevail for predicting spatial patterns of species assemblages. The first approach (macroecological modelling, MEM) focuses directly on realised properties of species assemblages, whereas the second approach (stacked species distribution modelling, S-SDM) starts with constituent species to approximate assemblage properties. Here, we propose to unify the two approaches in a single 'spatially-explicit species assemblage modelling' (SESAM) framework. This framework uses relevant species source pool designations, macroecological factors, and ecological assembly rules to constrain predictions of the richness and composition of species assemblages obtained by stacking predictions of individual species distributions. We believe that such a framework could prove useful in many theoretical and applied disciplines of ecology and evolution, both for improving our basic understanding of species assembly across spatio-temporal scales and for anticipating expected consequences of local, regional or global environmental changes. In this paper, we propose such a framework and call for further developments and testing across a broad range of community types in a variety of environments.

Addition of aripiprazole to the clozapine may be useful in reducing anxiety in treatment-resistant schizophrenia.

There exist many case reports and studies on the antipsychotic augmentation by aripirazole in partial responders to clozapine, the most seem to be finding a slight difference in the PANSS and CGI scores after the aripirazole addition. The results of our report are compatible with those of other studies but, we have found a considerable antianxiety action in both of the cases. The 5HT1A agonism of aripirazole could be hypothesized as mechanism contributing to this effect.

BIOMOD - a platform for ensemble forecasting of species distributions

BIOMOD is a computer platform for ensemble forecasting of species distributions, enabling the treatment of a range of methodological uncertainties in models and the examination of species-environment relationships. BIOMOD includes the ability to model species distributions with several techniques, test models with a wide range of approaches, project species distributions into different environmental conditions (e.g. climate or land use change scenarios) and dispersal functions. It allows assessing species temporal turnover, plot species response curves, and test the strength of species interactions with predictor variables. BIOMOD is implemented in R and is a freeware, open source, package

Unfolding nature "in silico" : overcoming practical and metodological limitations of species distribution models

Abstract : The existence of a causal relationship between the spatial distribution of living organisms and their environment, in particular climate, has been long recognized and is the central principle of biogeography. In turn, this recognition has led scientists to the idea of using the climatic, topographic, edaphic and biotic characteristics of the environment to predict its potential suitability for a given species or biological community. In this thesis, my objective is to contribute to the development of methodological improvements in the field of species distribution modeling. More precisely, the objectives are to propose solutions to overcome limitations of species distribution models when applied to conservation biology issues, or when .used as an assessment tool of the potential impacts of global change. The first objective of my thesis is to contribute to evidence the potential of species distribution models for conservation-related applications. I present a methodology to generate pseudo-absences in order to overcome the frequent lack of reliable absence data. I also demonstrate, both theoretically (simulation-based) and practically (field-based), how species distribution models can be successfully used to model and sample rare species. Overall, the results of this first part of the thesis demonstrate the strong potential of species distribution models as a tool for practical applications in conservation biology. The second objective this thesis is to contribute to improve .projections of potential climate change impacts on species distributions, and in particular for mountain flora. I develop and a dynamic model, MIGCLIM, that allows the implementation of dispersal limitations into classic species distribution models and present an application of this model to two virtual species. Given that accounting for dispersal limitations requires information on seed dispersal, distances, a general methodology to classify species into broad dispersal types is also developed. Finally, the M~GCLIM model is applied to a large number of species in a study area of the western Swiss Alps. Overall, the results indicate that while dispersal limitations can have an important impact on the outcome of future projections of species distributions under climate change scenarios, estimating species threat levels (e.g. species extinction rates) for a mountainous areas of limited size (i.e. regional scale) can also be successfully achieved when considering dispersal as unlimited (i.e. ignoring dispersal limitations, which is easier from a practical point of view). Finally, I present the largest fine scale assessment of potential climate change impacts on mountain vegetation that has been carried-out to date. This assessment involves vegetation from 12 study areas distributed across all major western and central European mountain ranges. The results highlight that some mountain ranges (the Pyrenees and the Austrian Alps) are expected to be more affected by climate change than others (Norway and the Scottish Highlands). The results I obtain in this study also indicate that the threat levels projected by fine scale models are less severe than those derived from coarse scale models. This result suggests that some species could persist in small refugias that are not detected by coarse scale models. Résumé : L'existence d'une relation causale entre la répartition des espèces animales et végétales et leur environnement, en particulier le climat, a été mis en évidence depuis longtemps et est un des principes centraux en biogéographie. Ce lien a naturellement conduit à l'idée d'utiliser les caractéristiques climatiques, topographiques, édaphiques et biotiques de l'environnement afin d'en prédire la qualité pour une espèce ou une communauté. Dans ce travail de thèse, mon objectif est de contribuer au développement d'améliorations méthodologiques dans le domaine de la modélisation de la distribution d'espèces dans le paysage. Plus précisément, les objectifs sont de proposer des solutions afin de surmonter certaines limitations des modèles de distribution d'espèces dans des applications pratiques de biologie de la conservation ou dans leur utilisation pour évaluer l'impact potentiel des changements climatiques sur l'environnement. Le premier objectif majeur de mon travail est de contribuer à démontrer le potentiel des modèles de distribution d'espèces pour des applications pratiques en biologie de la conservation. Je propose une méthode pour générer des pseudo-absences qui permet de surmonter le problème récurent du manque de données d'absences fiables. Je démontre aussi, de manière théorique (par simulation) et pratique (par échantillonnage de terrain), comment les modèles de distribution d'espèces peuvent être utilisés pour modéliser et améliorer l'échantillonnage des espèces rares. Ces résultats démontrent le potentiel des modèles de distribution d'espèces comme outils pour des applications de biologie de la conservation. Le deuxième objectif majeur de ce travail est de contribuer à améliorer les projections d'impacts potentiels des changements climatiques sur la flore, en particulier dans les zones de montagnes. Je développe un modèle dynamique de distribution appelé MigClim qui permet de tenir compte des limitations de dispersion dans les projections futures de distribution potentielle d'espèces, et teste son application sur deux espèces virtuelles. Vu que le fait de prendre en compte les limitations dues à la dispersion demande des données supplémentaires importantes (p.ex. la distance de dispersion des graines), ce travail propose aussi une méthode de classification simplifiée des espèces végétales dans de grands "types de disperseurs", ce qui permet ainsi de d'obtenir de bonnes approximations de distances de dispersions pour un grand nombre d'espèces. Finalement, j'applique aussi le modèle MIGCLIM à un grand nombre d'espèces de plantes dans une zone d'études des pré-Alpes vaudoises. Les résultats montrent que les limitations de dispersion peuvent avoir un impact considérable sur la distribution potentielle d'espèces prédites sous des scénarios de changements climatiques. Cependant, quand les modèles sont utilisés pour évaluer les taux d'extinction d'espèces dans des zones de montages de taille limitée (évaluation régionale), il est aussi possible d'obtenir de bonnes approximations en considérant la dispersion des espèces comme illimitée, ce qui est nettement plus simple d'un point dé vue pratique. Pour terminer je présente la plus grande évaluation à fine échelle d'impact potentiel des changements climatiques sur la flore des montagnes conduite à ce jour. Cette évaluation englobe 12 zones d'études réparties sur toutes les chaines de montages principales d'Europe occidentale et centrale. Les résultats montrent que certaines chaines de montagnes (les Pyrénées et les Alpes Autrichiennes) sont projetées comme plus sensibles aux changements climatiques que d'autres (les Alpes Scandinaves et les Highlands d'Ecosse). Les résultats obtenus montrent aussi que les modèles à échelle fine projettent des impacts de changement climatiques (p. ex. taux d'extinction d'espèces) moins sévères que les modèles à échelle large. Cela laisse supposer que les modèles a échelle fine sont capables de modéliser des micro-niches climatiques non-détectées par les modèles à échelle large.

Novel methods improve prediction of species' distributions from occurrence data

Prediction of species' distributions is central to diverse applications in ecology, evolution and conservation science. There is increasing electronic access to vast sets of occurrence records in museums and herbaria, yet little effective guidance on how best to use this information in the context of numerous approaches for modelling distributions. To meet this need, we compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date. We used presence-only data to fit models, and independent presence-absence data to evaluate the predictions. Along with well-established modelling methods such as generalised additive models and GARP and BIOCLIM, we explored methods that either have been developed recently or have rarely been applied to modelling species' distributions. These include machine-learning methods and community models, both of which have features that may make them particularly well suited to noisy or sparse information, as is typical of species' occurrence data. Presence-only data were effective for modelling species' distributions for many species and regions. The novel methods consistently outperformed more established methods. The results of our analysis are promising for the use of data from museums and herbaria, especially as methods suited to the noise inherent in such data improve.