Partitioning of reproduction in mother-daughter versus sibling associations : a test of optimal skew theory

A critical feature of cooperative animal societies is the reproductive skew, a shorthand term for the degree to which a dominant individual monopolizes overall reproduction in the group. Our theoretical analysis of the evolutionarily stable skew in matrifilial (i.e., mother-daughter) societies, in which relatednesses to offspring are asymmetrical, predicts that reproductive skews in such societies should tend to be greater than those of semisocial societies (i.e., societies composed of individuals of the same generation, such as siblings), in which relatednesses to offspring are symmetrical. Quantitative data on reproductive skews in semisocial and matrifilial associations within the same species for 17 eusocial Hymenoptera support this prediction. Likewise, a survey of reproductive partitioning within 20 vertebrate societies demonstrates that complete reproductive monopoly is more likely to occur in matrifilial than in semisocial societies, also as predicted by the optimal skew model.

Intervention précoce en Guidance Interactive et modification de la réactivité neuroendocrinienne chez l'ancien grand prématuré âgé de 12 mois et sa mère

L'étude a pour objectif de mettre en évidence les effets d'une intervention précoce inspirée des thérapies en Guidance Interactive sur la qualité de l'attachement ainsi que sur la réactivité neuroendocrinienne de stress chez des grands prématurés âgés de 12 mois ainsi que chez leurs mères. La population étudiée comprend 48 grands prématurés (<33 semaines de gestation) et leurs mères. Un programme d'intervention précoce a été proposé aléatoirement à la moitié des dyades incluses dans l'étude. Des mesures de cortisol salivaire ont été effectuées à 12 mois lors d'un épisode de stress modéré (la Situation Étrange) tant chez la mère que chez l'enfant. Les mères ayant bénéficié de l'intervention précoce montrent des taux de cortisol plus élevés que celles n'ayant pas bénéficié de l'intervention. Les auteurs font l'hypothèse que ces mères ont pu développer leur sensibilité envers leur enfant et se montrent, par conséquent, plus concernées lors de l'épisode de stress modéré. The present project aims to assess the effects of an early intervention inspired from Interactive Guidance therapy, on later attachment quality and stress reactivity of prematurely born infants and their mothers. The studied population contends 48 preterm born infants (< 33 weeks og gestational age). Half of the dyads receive an intervention program aiming at promoting the parents' responsivity-sensitivity to infant's cues. Infant's and mother's stress reactivity (salivary cortisol) to mild stressors (Strange Situation) will be assessed at 12 months. Mothers with intervention program show higher cortisol levels than the others. The authors postulate that these mothers enhance their caregiving quality and, subsequently, are more prone to be sensitive to infant's cues and to be concerned during the mild stress episode.

The genetical theory of social behaviour.

We survey the population genetic basis of social evolution, using a logically consistent set of arguments to cover a wide range of biological scenarios. We start by reconsidering Hamilton's (Hamilton 1964 J. Theoret. Biol. 7, 1-16 (doi:10.1016/0022-5193(64)90038-4)) results for selection on a social trait under the assumptions of additive gene action, weak selection and constant environment and demography. This yields a prediction for the direction of allele frequency change in terms of phenotypic costs and benefits and genealogical concepts of relatedness, which holds for any frequency of the trait in the population, and provides the foundation for further developments and extensions. We then allow for any type of gene interaction within and between individuals, strong selection and fluctuating environments and demography, which may depend on the evolving trait itself. We reach three conclusions pertaining to selection on social behaviours under broad conditions. (i) Selection can be understood by focusing on a one-generation change in mean allele frequency, a computation which underpins the utility of reproductive value weights; (ii) in large populations under the assumptions of additive gene action and weak selection, this change is of constant sign for any allele frequency and is predicted by a phenotypic selection gradient; (iii) under the assumptions of trait substitution sequences, such phenotypic selection gradients suffice to characterize long-term multi-dimensional stochastic evolution, with almost no knowledge about the genetic details underlying the coevolving traits. Having such simple results about the effect of selection regardless of population structure and type of social interactions can help to delineate the common features of distinct biological processes. Finally, we clarify some persistent divergences within social evolution theory, with respect to exactness, synergies, maximization, dynamic sufficiency and the role of genetic arguments.

Revising the Cannabis Use Disorders Identification Test (CUDIT) by means of Item Response Theory.

Cannabis use among adolescents and young adults has become a major public health challenge. Several European countries are currently developing short screening instruments to identify 'problematic' forms of cannabis use in general population surveys. One such instrument is the Cannabis Use Disorders Identification Test (CUDIT), a 10-item questionnaire based on the Alcohol Use Disorders Identification Test. Previous research found that some CUDIT items did not perform well psychometrically. In the interests of improving the psychometric properties of the CUDIT, this study replaces the poorly performing items with new items that specifically address cannabis use. Analyses are based on a sub-sample of 558 recent cannabis users from a representative population sample of 5722 individuals (aged 13-32) who were surveyed in the 2007 Swiss Cannabis Monitoring Study. Four new items were added to the original CUDIT. Psychometric properties of all 14 items, as well as the dimensionality of the supplemented CUDIT were then examined using Item Response Theory. Results indicate the unidimensionality of CUDIT and an improvement in its psychometric performance when three original items (usual hours being stoned; injuries; guilt) are replaced by new ones (motives for using cannabis; missing out leisure time activities; difficulties at work/school). However, improvements were limited to cannabis users with a high problem score. For epidemiological purposes, any further revision of CUDIT should therefore include a greater number of 'easier' items.

Behavioural alteration in chronic pain: are brain glia involved?

Behavioural symptoms such as abnormal emotionality (including anxious and depressive episodes) and cognition (for instance weakened decision-making) are highly frequent in both chronic pain patients and their animal models. The theory developed in the present article posits that alterations in glial cells (astrocytes and microglia) in cortical and limbic brain regions might be the origin of such emotional and cognitive chronic pain-associated impairments. Indeed, in mood disorders (unipolar depression, anxiety disorders, autism or schizophrenia) glial changes in brain regions involved in mood control (prefrontal and cingulate cortices, amygdala and the hippocampus) have been recurrently described. Besides, glial cells have been undoubtedly identified as key actors in the sensory component of chronic pain, owing to the profound phenotypical changes they undergo throughout the sensory pathway. Hence, the possibility arises that brain astrocytes and microglia react in upper brain structures as well, mediating the related mood and cognitive dysfunctions in chronic pain. So far, only very few studies have provided results in this prospect, mainly indirectly in pain-independent researches. Nevertheless, the first scant available data seem to merge in a unified description of a brain glial reaction occurring after chronic peripheral lesion. The present article uses this scarce literature to formulate the provocative theory of a glia-driven mood and cognitive dysfunction in chronic pain, expounding upon its validity and putative therapeutical impact as well as its current limitations and expected future developments.

Taking "Galton's problem" Seriously : Towards a Theory of Policy Diffusion

This article builds on the recent policy diffusion literature and attempts to overcome one of its major problems, namely the lack of a coherent theoretical framework. The literature defines policy diffusion as a process where policy choices are interdependent, and identifies several diffusion mechanisms that specify the link between the policy choices of the various actors. As these mechanisms are grounded in different theories, theoretical accounts of diffusion currently have little internal coherence. In this article we put forward an expected-utility model of policy change that is able to subsume all the diffusion mechanisms. We argue that the expected utility of a policy depends on both its effectiveness and the payoffs it yields, and we show that the various diffusion mechanisms operate by altering these two parameters. Each mechanism affects one of the two parameters, and does so in distinct ways. To account for aggregate patterns of diffusion, we embed our model in a simple threshold model of diffusion. Given the high complexity of the process that results, strong analytical conclusions on aggregate patterns cannot be drawn without more extensive analysis which is beyond the scope of this article. However, preliminary considerations indicate that a wide range of diffusion processes may exist and that convergence is only one possible outcome.

Statistical learning theory for geospatial data. Case study: Aral sea

In recent years there has been an explosive growth in the development of adaptive and data driven methods. One of the efficient and data-driven approaches is based on statistical learning theory (Vapnik 1998). The theory is based on Structural Risk Minimisation (SRM) principle and has a solid statistical background. When applying SRM we are trying not only to reduce training error ? to fit the available data with a model, but also to reduce the complexity of the model and to reduce generalisation error. Many nonlinear learning procedures recently developed in neural networks and statistics can be understood and interpreted in terms of the structural risk minimisation inductive principle. A recent methodology based on SRM is called Support Vector Machines (SVM). At present SLT is still under intensive development and SVM find new areas of application (www.kernel-machines.org). SVM develop robust and non linear data models with excellent generalisation abilities that is very important both for monitoring and forecasting. SVM are extremely good when input space is high dimensional and training data set i not big enough to develop corresponding nonlinear model. Moreover, SVM use only support vectors to derive decision boundaries. It opens a way to sampling optimization, estimation of noise in data, quantification of data redundancy etc. Presentation of SVM for spatially distributed data is given in (Kanevski and Maignan 2004).