Genital Chlamydia trachomatis: understanding the roles of innate and adaptive immunity in vaccine research.

Chlamydia trachomatis is the leading cause of bacterial sexually transmitted disease worldwide, and despite significant advances in chlamydial research, a prophylactic vaccine has yet to be developed. This Gram-negative obligate intracellular bacterium, which often causes asymptomatic infection, may cause pelvic inflammatory disease (PID), ectopic pregnancies, scarring of the fallopian tubes, miscarriage, and infertility when left untreated. In the genital tract, Chlamydia trachomatis infects primarily epithelial cells and requires Th1 immunity for optimal clearance. This review first focuses on the immune cells important in a chlamydial infection. Second, we summarize the research and challenges associated with developing a chlamydial vaccine that elicits a protective Th1-mediated immune response without inducing adverse immunopathologies.

Activation of the NLRP3 inflammasome in dendritic cells induces IL-1beta-dependent adaptive immunity against tumors.

The therapeutic efficacy of anticancer chemotherapies may depend on dendritic cells (DCs), which present antigens from dying cancer cells to prime tumor-specific interferon-gamma (IFN-gamma)-producing T lymphocytes. Here we show that dying tumor cells release ATP, which then acts on P2X(7) purinergic receptors from DCs and triggers the NOD-like receptor family, pyrin domain containing-3 protein (NLRP3)-dependent caspase-1 activation complex ('inflammasome'), allowing for the secretion of interleukin-1beta (IL-1beta). The priming of IFN-gamma-producing CD8+ T cells by dying tumor cells fails in the absence of a functional IL-1 receptor 1 and in Nlpr3-deficient (Nlrp3(-/-)) or caspase-1-deficient (Casp-1(-/-)) mice unless exogenous IL-1beta is provided. Accordingly, anticancer chemotherapy turned out to be inefficient against tumors established in purinergic receptor P2rx7(-/-) or Nlrp3(-/-) or Casp1(-/-) hosts. Anthracycline-treated individuals with breast cancer carrying a loss-of-function allele of P2RX7 developed metastatic disease more rapidly than individuals bearing the normal allele. These results indicate that the NLRP3 inflammasome links the innate and adaptive immune responses against dying tumor cells.

THE USEFUL SIDE OF AMBIVALENCE: Ambivalent attitudes can be adaptive and socially valued

En s'inspirant de la littérature récente qui a dépeint l'ambivalence comme étant adaptative et en lien avec des comportements stratégiques, cette thèse examine le versant utile des attitudes ambivalentes. Elle met tout d'abord en évidence que son expression peut-être sciemment contrôlée et mise en avant pour des raisons d'auto-présentation. De plus, elle démontre que les individus peuvent présenter une attitude ambivalente afin de gagner l'approbation sociale sur un objet d'attitude controversé alors que l'inverse a été observé sur des objets consensuels (Première ligne de recherche). Cette thèse a également révélé que l'expression d'attitudes ambivalentes pouvait amener à être valorisé socialement. En effet, contrairement à des attitudes plus tranchées (pro-normatives ou contre-normatives), les attitudes ambivalentes ont été évaluées de façon plus importante sur la dimension de l'utilité sociale (une dimension qui indique la compétence d'autrui ou encore la propension à évoluer dans la hiérarchie sociale). La valorisation de l'ambivalence n'est apparue que sur la dimension de l'utilité sociale et non sur la dimension de la désirabilité sociale (une dimension qui indique la sympathie d'autrui ainsi que la propension à être apprécié socialement). De plus, ce résultat a été observé sur des thèmes controversés et non sur des thèmes consensuels (Seconde ligne de recherche). Dans l'ensemble cette thèse soutient une approche de l'ambivalence comme donnant lieu à des bénéfices. Elle peut également ouvrir la voie à l'étude de l'ambivalence en lien avec la pensée critique. - Drawing on the recent literature that portrayed ambivalence as being adaptive and linked with strategic behaviors, this thesis examines the useful side of ambivalent attitudes. It first revealed that the expression of ambivalent attitudes could be controlled and purposely displayed for self-presentational concerns. Furthermore, it demonstrated that people could put ambivalence forward to gain social approval when expressing it on controversial social issues, whereas the opposite was true on consensual social issues (First line of research). The thesis also revealed that the expression of ambivalent attitudes could lead to be socially valued. Indeed, contrary to clear-cut attitudes (either pro-normative or counter-normative attitudes), ambivalent attitudes have been evaluated the highest on the social utility dimension (a dimension indicating people's competence as well as the extent to which they are likely to climb in social hierarchy). The valorization of ambivalent attitudes only appeared on social utility and not on social desirability (a dimension indicating people's niceness as well as the extent to which they are likely to be socially appreciated). This effect was true on controversial social issues but not on consensual ones (Second line of research). Overall, this thesis provides support for an approach that conceives attitudinal ambivalence as leading to benefits. It also may open avenues for the study of ambivalence in relation with critical thinking.

Cardiac raptor deletion impairs adaptive hypertrophy, alters metabolic gene expression and causes heart failure in mice

Background: Mammalian target of rapamycin (mTOR), a central regulator of cell growth, is found in two structurally and functionally distinct multiprotein complexes called mTOR complex (mTORC)1 and mTORC2. The specific roles of each of these branches of mTOR signaling have not been dissected in the adult heart. In the present study, we aimed to bring new insights into the function of cardiac mTORC1-mediated signaling in physiological as well as pathological situations.Methods: We generated mice homozygous for loxP-flanked raptor and positive for the tamoxifen-inducible Cre recombinase (MerCreMer) under control of the α- myosin heavy chain promoter. The raptor gene encodes an essential component of mTORC1. Gene ablation was induced at the age of 10-12 weeks, and two weeks later the raptor cardiac-knockout (raptor-cKO) mice started voluntary cagewheel exercise or were subjected to transverse aortic constriction (TAC) to induce pressure overload.Results: In sedentary raptor-cKO mice, ejection fractions gradually decreased, resulting in significantly reduced values at 38 days (P < 0.001). Raptor-cKO mice started to die during the fifth week after the last tamoxifen injection. At that time, the mortality rate was 36% in sedentary (n = 11) and 64% in exercising (n = 14) mice. TAC-induced pressure overload resulted in severe cardiac dysfunction already at earlier timepoints. Thus, at 7-9 days after surgery, ejection fraction and fractional shortening values were 22.3% vs 43.5% and 10.2% vs 21.5% in raptor-cKO vs wild-type mice, respectively. This was accompanied by significant reductions of ventricular wall and septal thickness as well as an increase in left ventricular internal diameter. Moreover, ventricular weight to tibial length ratios were increased in wild-type, but not in the raptor-cKO TAC mice. Together, this shows that raptor-cKO mice rapidly developed dilated cardiomyopathy without going through a phase of adaptive hypertrophy. Expression of ANP and β-MHC was induced in all raptor-cKO mice irrespective of the cardiac load conditions. Consistent with reduced mTORC1 activity, phosphorylation of ribosomal S6 kinase and 4E-BP1 was blunted, indicating reduced protein synthesis. Moreover, expression of multiple genes involved in the regulation of energy metabolism was altered, and followed by a shift from fatty acid to glucose oxidation.Conclusion: Our study suggests that mTORC1 coordinates protein and energy metabolic pathways in the heart. Moreover, we demonstrate that raptor is essential for the cardiac adaptation to increased workload and importantly, also for normal physiological cardiac function.

Direct numerical simulations of interface dynamics to link capillary pressure and total surface energy

The flow of two immiscible fluids through a porous medium depends on the complex interplay between gravity, capillarity, and viscous forces. The interaction between these forces and the geometry of the medium gives rise to a variety of complex flow regimes that are difficult to describe using continuum models. Although a number of pore-scale models have been employed, a careful investigation of the macroscopic effects of pore-scale processes requires methods based on conservation principles in order to reduce the number of modeling assumptions. In this work we perform direct numerical simulations of drainage by solving Navier-Stokes equations in the pore space and employing the Volume Of Fluid (VOF) method to track the evolution of the fluid-fluid interface. After demonstrating that the method is able to deal with large viscosity contrasts and model the transition from stable flow to viscous fingering, we focus on the macroscopic capillary pressure and we compare different definitions of this quantity under quasi-static and dynamic conditions. We show that the difference between the intrinsic phase-average pressures, which is commonly used as definition of Darcy-scale capillary pressure, is subject to several limitations and it is not accurate in presence of viscous effects or trapping. In contrast, a definition based on the variation of the total surface energy provides an accurate estimate of the macroscopic capillary pressure. This definition, which links the capillary pressure to its physical origin, allows a better separation of viscous effects and does not depend on the presence of trapped fluid clusters.

Update on the non-prewhitening model observer in computed tomography for the assessment of the adaptive statistical and model-based iterative reconstruction algorithms.

The state of the art to describe image quality in medical imaging is to assess the performance of an observer conducting a task of clinical interest. This can be done by using a model observer leading to a figure of merit such as the signal-to-noise ratio (SNR). Using the non-prewhitening (NPW) model observer, we objectively characterised the evolution of its figure of merit in various acquisition conditions. The NPW model observer usually requires the use of the modulation transfer function (MTF) as well as noise power spectra. However, although the computation of the MTF poses no problem when dealing with the traditional filtered back-projection (FBP) algorithm, this is not the case when using iterative reconstruction (IR) algorithms, such as adaptive statistical iterative reconstruction (ASIR) or model-based iterative reconstruction (MBIR). Given that the target transfer function (TTF) had already shown it could accurately express the system resolution even with non-linear algorithms, we decided to tune the NPW model observer, replacing the standard MTF by the TTF. It was estimated using a custom-made phantom containing cylindrical inserts surrounded by water. The contrast differences between the inserts and water were plotted for each acquisition condition. Then, mathematical transformations were performed leading to the TTF. As expected, the first results showed a dependency of the image contrast and noise levels on the TTF for both ASIR and MBIR. Moreover, FBP also proved to be dependent of the contrast and noise when using the lung kernel. Those results were then introduced in the NPW model observer. We observed an enhancement of SNR every time we switched from FBP to ASIR to MBIR. IR algorithms greatly improve image quality, especially in low-dose conditions. Based on our results, the use of MBIR could lead to further dose reduction in several clinical applications.

Adaptive tuning of perceptual timing to whole body motion.

In a previous work we have shown that sinusoidal whole-body rotations producing continuous vestibular stimulation, affected the timing of motor responses as assessed with a paced finger tapping (PFT) task (Binetti et al. (2010). Neuropsychologia, 48(6), 1842-1852). Here, in two new psychophysical experiments, one purely perceptual and one with both sensory and motor components, we explored the relationship between body motion/vestibular stimulation and perceived timing of acoustic events. In experiment 1, participants were required to discriminate sequences of acoustic tones endowed with different degrees of acceleration or deceleration. In this experiment we found that a tone sequence presented during acceleratory whole-body rotations required a progressive increase in rate in order to be considered temporally regular, consistent with the idea of an increase in "clock" frequency and of an overestimation of time. In experiment 2 participants produced self-paced taps, which entailed an acoustic feedback. We found that tapping frequency in this task was affected by periodic motion by means of anticipatory and congruent (in-phase) fluctuations irrespective of the self-generated sensory feedback. On the other hand, synchronizing taps to an external rhythm determined a completely opposite modulation (delayed/counter-phase). Overall this study shows that body displacements "remap" our metric of time, affecting not only motor output but also sensory input.

Uncovering the genetic basis of adaptive change: on the intersection of landscape genomics and theoretical population genetics

A workshop recently held at the Ecole Polytechnique Federale de Lausanne (EPFL, Switzerland) was dedicated to understanding the genetic basis of adaptive change, taking stock of the different approaches developed in theoretical population genetics and landscape genomics and bringing together knowledge accumulated in both research fields. Indeed, an important challenge in theoretical population genetics is to incorporate effects of demographic history and population structure. But important design problems (e.g. focus on populations as units, focus on hard selective sweeps, no hypothesis-based framework in the design of the statistical tests) reduce their capability of detecting adaptive genetic variation. In parallel, landscape genomics offers a solution to several of these problems and provides a number of advantages (e.g. fast computation, landscape heterogeneity integration). But the approach makes several implicit assumptions that should be carefully considered (e.g. selection has had enough time to create a functional relationship between the allele distribution and the environmental variable, or this functional relationship is assumed to be constant). To address the respective strengths and weaknesses mentioned above, the workshop brought together a panel of experts from both disciplines to present their work and discuss the relevance of combining these approaches, possibly resulting in a joint software solution in the future.

Evidence for morphological and adaptive genetic divergence between lake and stream habitats in European minnows (Phoxinus phoxinus, Cyprinidae).

Natural selection drives local adaptation, potentially even at small temporal and spatial scales. As a result, adaptive genetic and phenotypic divergence can occur among populations living in different habitats. We investigated patterns of differentiation between contrasting lake and stream habitats in the cyprinid fish European minnow (Phoxinus phoxinus) at both the morphological and genomic levels using geometric morphometrics and AFLP markers, respectively. We also used a spatial correlative approach to identify AFLP loci associated with environmental variables representing potential selective forces responsible for adaptation to divergent habitats. Our results identified different morphologies between lakes and streams, with lake fish presenting a deeper body and caudal peduncle compared to stream fish. Body shape variation conformed to a priori predictions concerning biomechanics and swimming performance in lakes vs. streams. Moreover, morphological differentiation was found to be associated with several environmental variables, which could impose selection on body and caudal peduncle shape. We found adaptive genetic divergence between these contrasting habitats in the form of 'outlier' loci (2.9%) whose genetic divergence exceeded neutral expectations. We also detected additional loci (6.6%) not associated with habitat type (lake vs. stream), but contributing to genetic divergence between populations. Specific environmental variables related to trophic dynamics, landscape topography and geography were associated with several neutral and outlier loci. These results provide new insights into the morphological divergence and genetic basis of adaptation to differentiated habitats.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Cross-frequency coupling in EEG revealed by adaptive oscillation tracking.

Introduction: Neuronal oscillations have been the focus of increasing interest in the neuroscientific community, in part because they have been considered as a possible integrating mechanism through which internal states can influence stimulus processing in a top-down way (Engel et al., 2001). Moreover, increasing evidence indicates that oscillations in different frequency bands interact with one other through coupling mechanisms (Jensen and Colgin, 2007). The existence and the importance of these cross-frequency couplings during various tasks have been verified by recent studies (Canolty et al., 2006; Lakatos et al., 2007). In this study, we measure the strength and directionality of two types of couplings - phase-amplitude couplings and phase-phase couplings - between various bands in EEG data recorded during an illusory contour experiment that were identified using a recently-proposed adaptive frequency tracking algorithm (Van Zaen et al., 2010). Methods: The data used in this study have been taken from a previously published study examining the spatiotemporal mechanisms of illusory contour processing (Murray et al., 2002). The EEG in the present study were from a subset of nine subjects. Each stimulus was composed of 'pac-man' inducers presented in two orientations: IC, when an illusory contour was present, and NC, when no contour could be detected. The signals recorded by the electrodes P2, P4, P6, PO4 and PO6 were averaged, and filtered into the following bands: 4-8Hz, 8-12Hz, 15-25Hz, 35-45Hz, 45-55Hz, 55-65Hz and 65-75Hz. An adaptive frequency tracking algorithm (Van Zaen et al., 2010) was then applied in each band in order to extract the main oscillation and estimate its frequency. This additional step ensures that clean phase information is obtained when taking the Hilbert transform. The frequency estimated by the tracker was averaged over sliding windows and then used to compare the two conditions. Two types of cross-frequency couplings were considered: phase-amplitude couplings and phase-phase couplings. Both types were measured with the phase locking value (PLV, Lachaux et al., 1999) over sliding windows. The phase-amplitude couplings were computed with the phase of the low frequency oscillation and the phase of the amplitude of the high frequency one. Different coupling coefficients were used when measuring phase-phase couplings in order to estimate different m:n synchronizations (4:3, 3:2, 2:1, 3:1, 4:1, 5:1, 6:1, 7:1, 8:1 and 9:1) and to take into account the frequency differences across bands. Moreover, the direction of coupling was estimated with a directionality index (Bahraminasab et al., 2008). Finally, the two conditions IC and NC were compared with ANOVAs with 'subject' as a random effect and 'condition' as a fixed effect. Before computing the statistical tests, the PLV values were transformed into approximately normal variables (Penny et al., 2008). Results: When comparing the mean estimated frequency across conditions, a significant difference was found only in the 4-8Hz band, such that the frequency within this band was significantly higher for IC than NC stimuli starting at ~250ms post-stimulus onset (Fig. 1; solid line shows IC and dashed line NC). Significant differences in phase-amplitude couplings were obtained only when the 4-8 Hz band was taken as the low frequency band. Moreover, in all significant situations, the coupling strength is higher for the NC than IC condition. An example of significant difference between conditions is shown in Fig. 2 for the phase-amplitude coupling between the 4-8Hz and 55-65Hz bands (p-value in top panel and mean PLV values in the bottom panel). A decrease in coupling strength was observed shortly after stimulus onset for both conditions and was greater for the condition IC. This phenomenon was observed with all other frequency bands. The results obtained for the phase-phase couplings were more complex. As for the phase-amplitude couplings, all significant differences were obtained when the 4-8Hz band was considered as the low frequency band. The stimulus condition exhibiting the higher coupling strength depended on the ratio of the coupling coefficients. When this ratio was small, the IC condition exhibited the higher phase-phase coupling strength. When this ratio was large, the NC condition exhibited the higher coupling strength. Fig. 3 shows the phase-phase couplings between the 4-8Hz and 35-45Hz bands for the coupling coefficient 6:1, and the coupling strength was significantly higher for the IC than NC condition. By contrast, for the coupling coefficient 9:1 the NC condition gave the higher coupling strength (Fig. 4). Control analyses verified that it is not a consequence of the frequency difference between the two conditions in the 4-8Hz band. The directionality measures indicated a transfer of information from the low frequency components towards the high frequency ones. Conclusions: Adaptive tracking is a feasible method for EEG analyses, revealing information both about stimulus-related differences and coupling patterns across frequencies. Theta oscillations play a central role in illusory shape processing and more generally in visual processing. The presence vs. absence of illusory shapes was paralleled by faster theta oscillations. Phase-amplitude couplings were decreased more for IC than NC and might be due to a resetting mechanism. The complex patterns in phase-phase coupling between theta and beta/gamma suggest that the contribution of these oscillations to visual binding and stimulus processing are not as straightforward as conventionally held. Causality analyses further suggest that theta oscillations drive beta/gamma oscillations (see also Schroeder and Lakatos, 2009). The present findings highlight the need for applying more sophisticated signal analyses in order to establish a fuller understanding of the functional role of neural oscillations.

MHC Class II Transactivator Is an In Vivo Regulator of Osteoclast Differentiation and Bone Homeostasis Co-opted From Adaptive Immunity.

The molecular networks controlling bone homeostasis are not fully understood. The common evolution of bone and adaptive immunity encourages the investigation of shared regulatory circuits. MHC Class II Transactivator (CIITA) is a master transcriptional co-activator believed to be exclusively dedicated for antigen presentation. CIITA is expressed in osteoclast precursors, and its expression is accentuated in osteoporotic mice. We thus asked whether CIITA plays a role in bone biology. To this aim, we fully characterized the bone phenotype of two mouse models of CIITA overexpression, respectively systemic and restricted to the monocyte-osteoclast lineage. Both CIITA-overexpressing mouse models revealed severe spontaneous osteoporosis, as assessed by micro-computed tomography and histomorphometry, associated with increased osteoclast numbers and enhanced in vivo bone resorption, whereas osteoblast numbers and in vivo bone-forming activity were unaffected. To understand the underlying cellular and molecular bases, we investigated ex vivo the differentiation of mutant bone marrow monocytes into osteoclasts and immune effectors, as well as osteoclastogenic signaling pathways. CIITA-overexpressing monocytes differentiated normally into effector macrophages or dendritic cells but showed enhanced osteoclastogenesis, whereas CIITA ablation suppressed osteoclast differentiation. Increased c-fms and receptor activator of NF-κB (RANK) signaling underlay enhanced osteoclast differentiation from CIITA-overexpressing precursors. Moreover, by extending selected phenotypic and cellular analyses to additional genetic mouse models, namely MHC Class II deficient mice and a transgenic mouse line lacking a specific CIITA promoter and re-expressing CIITA in the thymus, we excluded MHC Class II expression and T cells from contributing to the observed skeletal phenotype. Altogether, our study provides compelling genetic evidence that CIITA, the molecular switch of antigen presentation, plays a novel, unexpected function in skeletal homeostasis, independent of MHC Class II expression and T cells, by exerting a selective and intrinsic control of osteoclast differentiation and bone resorption in vivo. © 2014 American Society for Bone and Mineral Research.

Clypeal patterning in the paper wasp Polistes dominulus: no evidence of adaptive value in the wild

Status signals function in a number of species to communicate competitive ability to conspecific rivals during competition for resources. In the paper wasp Polistes dominulus, variable black clypeal patterns are thought to be important in mediating competition among females. Results of previous behavioral experiments in the lab indicate that P dominulus clypeal patterns provide information about an individual's competitive ability to rivals during agonistic interactions. To date, however, there has been no detailed examination of the adaptive value of clypeal patterns in the wild. To address this, we looked for correlations between clypeal patterning and various fitness measures, including reproductive success, hierarchical rank, and survival, in a large, free-living population of P. dominulus in southern Spain. Reproductive success over the nesting season was not correlated with clypeal patterning. Furthermore, there was no relationship between a female's clypeal patterning and the rank she achieved within the hierarchy or her survival during nest founding. Overall, we found no evidence that P dominulus clypeal patterns are related to competitive ability or other aspects of quality in our population. This result is consistent with geographical variation in the adaptive value of clypeal patterns between P. dominulus populations; however, data on the relationship between patterning and fitness from other populations are required to test this hypothesis.