Developing methodologies to allow the robust prediction dynamic context dominated by perturbations

El principal objectiu del projecte era desenvolupar millores conceptuals i metodològiques que permetessin una millor predicció dels canvis en la distribució de les espècies (a una escala de paisatge) derivats de canvis ambientals en un context dominat per pertorbacions. En un primer estudi, vàrem comparar l'eficàcia de diferents models dinàmics per a predir la distribució de l'hortolà (Emberiza hortulana). Els nostres resultats indiquen que un model híbrid que combini canvis en la qualitat de l'hàbitat, derivats de canvis en el paisatge, amb un model poblacional espacialment explícit és una aproximació adequada per abordar canvis en la distribució d'espècies en contextos de dinàmica ambiental elevada i una capacitat de dispersió limitada de l'espècie objectiu. En un segon estudi abordarem la calibració mitjançant dades de seguiment de models de distribució dinàmics per a 12 espècies amb preferència per hàbitats oberts. Entre les conclusions extretes destaquem: (1) la necessitat de que les dades de seguiment abarquin aquelles àrees on es produeixen els canvis de qualitat; (2) el biaix que es produeix en la estimació dels paràmetres del model d'ocupació quan la hipòtesi de canvi de paisatge o el model de qualitat d'hàbitat són incorrectes. En el darrer treball estudiarem el possible impacte en 67 espècies d’ocells de diferents règims d’incendis, definits a partir de combinacions de nivells de canvi climàtic (portant a un augment esperat de la mida i freqüència d’incendis forestals), i eficiència d’extinció per part dels bombers. Segons els resultats dels nostres models, la combinació de factors antropogènics del regim d’incendis, tals com l’abandonament rural i l’extinció, poden ser més determinants per als canvis de distribució que els efectes derivats del canvi climàtic. Els productes generats inclouen tres publicacions científiques, una pàgina web amb resultats del projecte i una llibreria per a l'entorn estadístic R.

Role of the cannabinoid system in the effects induced by nicotine on anxiety-like behaviour in mice

Rationale: Acute behavioural effects and motivational responses induced by nicotine can be modulated by the endocannabinoid system supporting the existence of a physiological interaction between these two systems. Objectives: The present study was designed to examine the possible involvement of the cannabinoid system in the anxiolytic- and anxiogenic-like responses induced by nicotine in mice. Methods: Animals were only exposed once to nicotine. The acute administration of low (0.05, sc) or high (0.8 mg/kg, sc) doses of nicotine produced opposite effects in the elevated plus-maze, i.e., anxiolytic- and anxiogenic-like responses, respectively. The effects of the pretreatment with the CB1 cannabinoid receptor antagonist, rimonabant (0.25, 0.5 and 1 mg/kg, ip), and the cannabinoid agonist, 9-tetrahydrocannabinol (0.1 mg/kg, ip), were evaluated on the anxiolytic- and anxiogenic-like responses induced by nicotine. Results: Rimonabant completely abolished nicotine-induced anxiolytic-like effects and increased the anxiogenic-like responses of nicotine, suggesting an involvement of CB1 receptors in these behavioural responses. On the other hand, 9-tetrahydrocannabinol failed to modify nicotine anxiolytic-like responses, but attenuated its anxiogenic-like effects. In addition the association of non-effective doses of 9-tetrahydrocannabinol and nicotine produced clear anxiolytic-like responses. Conclusions: These results demonstrate that the endogenous cannabinoid system is involved in the regulation of nicotine anxiety-like behaviour in mice, and provide new findings to support the use of cannabinoid antagonists in the treatment of tobacco addiction.

Knowledge management for systems biology a general and visually driven framework applied to translational medicine

Background: To enhance our understanding of complex biological systems like diseases we need to put all of the available data into context and use this to detect relations, pattern and rules which allow predictive hypotheses to be defined. Life science has become a data rich science with information about the behaviour of millions of entities like genes, chemical compounds, diseases, cell types and organs, which are organised in many different databases and/or spread throughout the literature. Existing knowledge such as genotype - phenotype relations or signal transduction pathways must be semantically integrated and dynamically organised into structured networks that are connected with clinical and experimental data. Different approaches to this challenge exist but so far none has proven entirely satisfactory. Results: To address this challenge we previously developed a generic knowledge management framework, BioXM™, which allows the dynamic, graphic generation of domain specific knowledge representation models based on specific objects and their relations supporting annotations and ontologies. Here we demonstrate the utility of BioXM for knowledge management in systems biology as part of the EU FP6 BioBridge project on translational approaches to chronic diseases. From clinical and experimental data, text-mining results and public databases we generate a chronic obstructive pulmonary disease (COPD) knowledge base and demonstrate its use by mining specific molecular networks together with integrated clinical and experimental data. Conclusions: We generate the first semantically integrated COPD specific public knowledge base and find that for the integration of clinical and experimental data with pre-existing knowledge the configuration based set-up enabled by BioXM reduced implementation time and effort for the knowledge base compared to similar systems implemented as classical software development projects. The knowledgebase enables the retrieval of sub-networks including protein-protein interaction, pathway, gene - disease and gene - compound data which are used for subsequent data analysis, modelling and simulation. Pre-structured queries and reports enhance usability; establishing their use in everyday clinical settings requires further simplification with a browser based interface which is currently under development.

Assembling genes from predicted exons in linear time with dynamic programming

In a number of programs for gene structure prediction in higher eukaryotic genomic sequences, exon prediction is decoupled from gene assembly: a large pool of candidate exons is predicted and scored from features located in the query DNA sequence, and candidate genes are assembled from such a pool as sequences of nonoverlapping frame-compatible exons. Genes are scored as a function of the scores of the assembled exons, and the highest scoring candidate gene is assumed to be the most likely gene encoded by the query DNA sequence. Considering additive gene scoring functions, currently available algorithms to determine such a highest scoring candidate gene run in time proportional to the square of the number of predicted exons. Here, we present an algorithm whose running time grows only linearly with the size of the set of predicted exons. Polynomial algorithms rely on the fact that, while scanning the set of predicted exons, the highest scoring gene ending in a given exon can be obtained by appending the exon to the highest scoring among the highest scoring genes ending at each compatible preceding exon. The algorithm here relies on the simple fact that such highest scoring gene can be stored and updated. This requires scanning the set of predicted exons simultaneously by increasing acceptor and donor position. On the other hand, the algorithm described here does not assume an underlying gene structure model. Indeed, the definition of valid gene structures is externally defined in the so-called Gene Model. The Gene Model specifies simply which gene features are allowed immediately upstream which other gene features in valid gene structures. This allows for great flexibility in formulating the gene identification problem. In particular it allows for multiple-gene two-strand predictions and for considering gene features other than coding exons (such as promoter elements) in valid gene structures.

From SNPs to pathways: integration of functional effect of sequence variations on models of cell signalling pathways

Background: Single nucleotide polymorphisms (SNPs) are the most frequent type of sequence variation between individuals, and represent a promising tool for finding genetic determinants of complex diseases and understanding the differences in drug response. In this regard, it is of particular interest to study the effect of non-synonymous SNPs in the context of biological networks such as cell signalling pathways. UniProt provides curated information about the functional and phenotypic effects of sequence variation, including SNPs, as well as on mutations of protein sequences. However, no strategy has been developed to integrate this information with biological networks, with the ultimate goal of studying the impact of the functional effect of SNPs in the structure and dynamics of biological networks. Results: First, we identified the different challenges posed by the integration of the phenotypic effect of sequence variants and mutations with biological networks. Second, we developed a strategy for the combination of data extracted from public resources, such as UniProt, NCBI dbSNP, Reactome and BioModels. We generated attribute files containing phenotypic and genotypic annotations to the nodes of biological networks, which can be imported into network visualization tools such as Cytoscape. These resources allow the mapping and visualization of mutations and natural variations of human proteins and their phenotypic effect on biological networks (e.g. signalling pathways, protein-protein interaction networks, dynamic models). Finally, an example on the use of the sequence variation data in the dynamics of a network model is presented. Conclusion: In this paper we present a general strategy for the integration of pathway and sequence variation data for visualization, analysis and modelling purposes, including the study of the functional impact of protein sequence variations on the dynamics of signalling pathways. This is of particular interest when the SNP or mutation is known to be associated to disease. We expect that this approach will help in the study of the functional impact of disease-associated SNPs on the behaviour of cell signalling pathways, which ultimately will lead to a better understanding of the mechanisms underlying complex diseases.

Dynamic estimation of three-dimensional cerebrovascular deformation from rotational angiography

Purpose: The objective of this study is to investigate the feasibility of detecting and quantifying 3D cerebrovascular wall motion from a single 3D rotational x-ray angiography (3DRA) acquisition within a clinically acceptable time and computing from the estimated motion field for the further biomechanical modeling of the cerebrovascular wall. Methods: The whole motion cycle of the cerebral vasculature is modeled using a 4D B-spline transformation, which is estimated from a 4D to 2D + t image registration framework. The registration is performed by optimizing a single similarity metric between the entire 2D + t measured projection sequence and the corresponding forward projections of the deformed volume at their exact time instants. The joint use of two acceleration strategies, together with their implementation on graphics processing units, is also proposed so as to reach computation times close to clinical requirements. For further characterizing vessel wall properties, an approximation of the wall thickness changes is obtained through a strain calculation. Results: Evaluation on in silico and in vitro pulsating phantom aneurysms demonstrated an accurate estimation of wall motion curves. In general, the error was below 10% of the maximum pulsation, even in the situation when substantial inhomogeneous intensity pattern was present. Experiments on in vivo data provided realistic aneurysm and vessel wall motion estimates, whereas in regions where motion was neither visible nor anatomically possible, no motion was detected. The use of the acceleration strategies enabled completing the estimation process for one entire cycle in 5-10 min without degrading the overall performance. The strain map extracted from our motion estimation provided a realistic deformation measure of the vessel wall. Conclusions: The authors' technique has demonstrated that it can provide accurate and robust 4D estimates of cerebrovascular wall motion within a clinically acceptable time, although it has to be applied to a larger patient population prior to possible wide application to routine endovascular procedures. In particular, for the first time, this feasibility study has shown that in vivo cerebrovascular motion can be obtained intraprocedurally from a 3DRA acquisition. Results have also shown the potential of performing strain analysis using this imaging modality, thus making possible for the future modeling of biomechanical properties of the vascular wall.

Multiuser detection in a dynamic environment — Part II: Joint user identification and parameter estimation

The problem of jointly estimating the number, the identities, and the data of active users in a time-varying multiuser environment was examined in a companion paper (IEEE Trans. Information Theory, vol. 53, no. 9, September 2007), at whose core was the use of the theory of finite random sets on countable spaces. Here we extend that theory to encompass the more general problem of estimating unknown continuous parameters of the active-user signals. This problem is solved here by applying the theory of random finite sets constructed on hybrid spaces. We doso deriving Bayesian recursions that describe the evolution withtime of a posteriori densities of the unknown parameters and data.Unlike in the above cited paper, wherein one could evaluate theexact multiuser set posterior density, here the continuous-parameter Bayesian recursions do not admit closed-form expressions. To circumvent this difficulty, we develop numerical approximationsfor the receivers that are based on Sequential Monte Carlo (SMC)methods (“particle filtering”). Simulation results, referring to acode-divisin multiple-access (CDMA) system, are presented toillustrate the theory.

Stochastic Dynamic Northern Corn Rootworm Population Model, January 2000

A complete life cycle model for northern corn rootworm, Diabrotica barberi Smith and Lawrence, is developed using a published single-season model of adult population dynamics and data from field experiments. Temperature-dependent development and age-dependent advancement determine adult population dynamics and oviposition, while a simple stochastic hatch and density-dependent larval survival model determine adult emergence. Dispersal is not modeled. To evaluate the long-run performance of the model, stochastically generated daily air and soil temperatures are used for 100-year simulations for a variety of corn planting and flowering dates in Ithaca, NY, and Brookings, SD. Once the model is corrected for a bias in oviposition, model predictions for both locations are consistent with anecdotal field data. Extinctions still occur, but these may be consistent with northern corn rootworm metapopulation dynamics.

Large-system analysis of a CDMA dynamic channel under a Markovian input process

We study the minimum mean square error (MMSE) and the multiuser efficiency η of large dynamic multiple access communication systems in which optimal multiuser detection is performed at the receiver as the number and the identities of active users is allowed to change at each transmission time. The system dynamics are ruled by a Markov model describing the evolution of the channel occupancy and a large-system analysis is performed when the number of observations grow large. Starting on the equivalent scalar channel and the fixed-point equation tying multiuser efficiency and MMSE, we extend it to the case of a dynamic channel, and derive lower and upper bounds for the MMSE (and, thus, for η as well) holding true in the limit of large signal–to–noise ratios and increasingly large observation time T.

The dermatophyte species Arthroderma benhamiae: intraspecies variability and mating behaviour.

Arthroderma benhamiae is a zoophilic dermatophyte belonging to the Trichophyton mentagrophytes species complex. Here, a population of A. benhamiae wild strains from the same geographical area (Switzerland) was studied by comparing their morphology, assessing their molecular variability using internal transcribed spacer (ITS) and 28S rRNA gene sequencing, and evaluating their interfertility. Sequencing of the ITS region and of part of the 28S rRNA gene revealed the existence of two infraspecific groups with markedly different colony phenotypes: white (group I) and yellow (group II), respectively. For all strains, the results of mating type identification by PCR, using HMG (high-mobility group) and α-box genes in the mating type locus as targets, were in total accordance with the results of mating type identification by strain confrontation experiments. White-phenotype strains were of mating type + (mt+) or mating type - (mt-), whilst yellow-phenotype strains were all mt-. White and yellow strains were found to produce fertile cleistothecia after mating with A. benhamiae reference tester strains, which belonged to a third group intermediate between groups I and II. However, no interfertility was observed between yellow strains and white strains of mt+. A significant result was that white strains of mt- were able to mate and produce fertile cleistothecia with the white A. benhamiae strain CBS 112371 (mt+), the genome of which has recently been sequenced and annotated. This finding should offer new tools for investigating the biology and genetics of dermatophytes using wild-type strains.

Alliance patterns over the course of short-term dynamic psychotherapy: The shape of productive relationships.

The shape of alliance processes over the course of psychotherapy has already been studied in several process-outcome studies on very brief psychotherapy. The present study applies the shape-of-change methodology to short-term dynamic psychotherapies and complements this method with hierarchical linear modeling. A total of 50 psychotherapies of up to 40 sessions were included. Alliance was measured at the end of each session. The results indicate that a linear progression model is most adequate. Three main patterns were found: stable, linear, and quadratic growth. The linear growth pattern, along with the slope parameter, was related to treatment outcome. This study sheds additional light on alliance process research, underscores the importance of linear alliance progression for outcome, and also fosters a better understanding of its limitations.

Experimental evidence that adult antipredator behaviour is heritable and not influenced by behavioural copying in a wild bird.

Knowledge of the relative importance of genetics and behavioural copying is crucial to appraise the evolvability of behavioural consistencies. Yet, genetic and non-genetic factors are often deeply intertwined, and experiments are required to address this issue. We investigated the sources of variation of adult antipredator behaviour in the Alpine swift (Apus melba) by making use of long-term behavioural observations on parents and cross-fostered offspring. By applying an 'animal model' approach to observational data, we show that antipredator behaviour of adult Alpine swifts was significantly repeatable over lifetime (r = 0.273) and heritable (h(2) = 0.146). Regression models also show that antipredator behaviours differed between colonies and sexes (females were more tame), and varied with the hour and year of capture. By applying a parent-offspring regression approach to 59 offspring that were exchanged as eggs or hatchlings between pairs of nests, we demonstrate that offspring behaved like their biological parents rather than like their foster parents when they were adults themselves. Those findings provide strong evidence that antipredator behaviour of adult Alpine swifts is shaped by genetics and/or pre-hatching maternal effects taking place at conception but not by behavioural copying.

Identity, immigration, and prejudice in Europe: a recognition approach

Social identity is a double-edged sword. On the one hand, identifying with a social group is a prerequisite for the sharing of common norms and values, solidarity, and collective action. On the other hand, in-group identification often goes together with prejudice and discrimination. Today, these two sides of social identification underlie contradictory trends in the way European nations and European nationals relate to immigrants and immigration. Most European countries are becoming increasingly multicultural, and anti-discrimination laws have been adopted throughout the European Union, demonstrating a normative shift towards more social inclusion and tolerance. At the same time, racist and xenophobic attitudes still shape social relations, individual as well as collective behaviour (both informal and institutional), and political positions throughout Europe. The starting point for this chapter is Sanchez-Mazas' (2004) interactionist approach to the study of racism and xenophobia, which in turn builds on Axel Honneth's (1996) philosophical theory of recognition. In this view, the origin of attitudes towards immigrants cannot be located in one or the other group, but in a dynamic of mutual influence. Sanchez-Mazas' approach is used as a general framework into which we integrate social psychological approaches of prejudice and recent empirical findings examining minority-majority relations. We particularly focus on the role of national and European identities as antecedents of anti-immigrant attitudes held by national majorities. Minorities' reactions to denials of recognition are also examined. We conclude by delineating possible social and political responses to prejudice towards immigrants.

Using robots to understand social behaviour.

A major challenge in studying social behaviour stems from the need to disentangle the behaviour of each individual from the resulting collective. One way to overcome this problem is to construct a model of the behaviour of an individual, and observe whether combining many such individuals leads to the predicted outcome. This can be achieved by using robots. In this review we discuss the strengths and weaknesses of such an approach for studies of social behaviour. We find that robots-whether studied in groups of simulated or physical robots, or used to infiltrate and manipulate groups of living organisms-have important advantages over conventional individual-based models and have contributed greatly to the study of social behaviour. In particular, robots have increased our understanding of self-organization and the evolution of cooperative behaviour and communication. However, the resulting findings have not had the desired impact on the biological community. We suggest reasons for why this may be the case, and how the benefits of using robots can be maximized in future research on social behaviour.