Regulation of adaptive behaviour during fasting by hypothalamic Foxa2.

The lateral hypothalamic area is considered the classic 'feeding centre', regulating food intake, arousal and motivated behaviour through the actions of orexin and melanin-concentrating hormone (MCH). These neuropeptides are inhibited in response to feeding-related signals and are released during fasting. However, the molecular mechanisms that regulate and integrate these signals remain poorly understood. Here we show that the forkhead box transcription factor Foxa2, a downstream target of insulin signalling, regulates the expression of orexin and MCH. During fasting, Foxa2 binds to MCH and orexin promoters and stimulates their expression. In fed and in hyperinsulinemic obese mice, insulin signalling leads to nuclear exclusion of Foxa2 and reduced expression of MCH and orexin. Constitutive activation of Foxa2 in the brain (Nes-Cre/+;Foxa2T156A(flox/flox) genotype) results in increased neuronal MCH and orexin expression and increased food consumption, metabolism and insulin sensitivity. Spontaneous physical activity of these animals in the fed state is significantly increased and is similar to that in fasted mice. Conditional activation of Foxa2 through the T156A mutation expression in the brain of obese mice also resulted in improved glucose homeostasis, decreased fat and increased lean body mass. Our results demonstrate that Foxa2 can act as a metabolic sensor in neurons of the lateral hypothalamic area to integrate metabolic signals, adaptive behaviour and physiological responses.

Clustering or scattering: the underlying reason for regulating distance among retail outlets

Concerns on the clustering of retail industries and professional services in main streets had traditionally been the public interest rationale for supporting distance regulations. Although many geographic restrictions have been suppressed, deregulation has hinged mostly upon the theory results on the natural tendency of outlets to differentiate spatially. Empirical evidence has so far offered mixed results. Using the case of deregulation of pharmacy establishment in a region of Spain, we empirically show how pharmacy locations scatter, and that there is not rationale for distance regulation apart from the underlying private interest of very few incumbents.

The role of chemokines in cancer immune surveillance by the adaptive immune system.

Chemokines are key molecules involved in the migration and homeostasis of immune cells. However, also tumor cells use chemokine signals for different processes such as tumor progression and metastasis. It is thus unclear whether chemokines, through their immunostimulatory roles, contribute to the repression of tumor cells by tumor immunosurveillance or whether chemokines act primarily as growth factors and chemoattractants for primary and metastatizing tumors, respectively. Research of recent years, using gene knockout mice, recombinant chemokines, and agents able to block chemokine actions, has provided further insight into the diverse functions of chemokines. Here, we review the current knowledge on the complex actions of chemokines at the interface of the immune system and the tumor.

Local-global splitting for spatiotemporal-adaptive multiscale methods

We present a novel spatiotemporal-adaptive Multiscale Finite Volume (MsFV) method, which is based on the natural idea that the global coarse-scale problem has longer characteristic time than the local fine-scale problems. As a consequence, the global problem can be solved with larger time steps than the local problems. In contrast to the pressure-transport splitting usually employed in the standard MsFV approach, we propose to start directly with a local-global splitting that allows to locally retain the original degree of coupling. This is crucial for highly non-linear systems or in the presence of physical instabilities. To obtain an accurate and efficient algorithm, we devise new adaptive criteria for global update that are based on changes of coarse-scale quantities rather than on fine-scale quantities, as it is routinely done before in the adaptive MsFV method. By means of a complexity analysis we show that the adaptive approach gives a noticeable speed-up with respect to the standard MsFV algorithm. In particular, it is efficient in case of large upscaling factors, which is important for multiphysics problems. Based on the observation that local time stepping acts as a smoother, we devise a self-correcting algorithm which incorporates the information from previous times to improve the quality of the multiscale approximation. We present results of multiphase flow simulations both for Darcy-scale and multiphysics (hybrid) problems, in which a local pore-scale description is combined with a global Darcy-like description. The novel spatiotemporal-adaptive multiscale method based on the local-global splitting is not limited to porous media flow problems, but it can be extended to any system described by a set of conservation equations.

Comparing Multilevel Clustering Methods on Weighted Graphs : The Case of Worldwide Air Passenger Traffic 2000-2004

Specific properties emerge from the structure of large networks, such as that of worldwide air traffic, including a highly hierarchical node structure and multi-level small world sub-groups that strongly influence future dynamics. We have developed clustering methods to understand the form of these structures, to identify structural properties, and to evaluate the effects of these properties. Graph clustering methods are often constructed from different components: a metric, a clustering index, and a modularity measure to assess the quality of a clustering method. To understand the impact of each of these components on the clustering method, we explore and compare different combinations. These different combinations are used to compare multilevel clustering methods to delineate the effects of geographical distance, hubs, network densities, and bridges on worldwide air passenger traffic. The ultimate goal of this methodological research is to demonstrate evidence of combined effects in the development of an air traffic network. In fact, the network can be divided into different levels of âeurooecohesionâeuro, which can be qualified and measured by comparative studies (Newman, 2002; Guimera et al., 2005; Sales-Pardo et al., 2007).

Hybridisation and diversification in the adaptive radiation of clownfishes.

BackgroundThe importance of hybridisation during species diversification has long been debated among evolutionary biologists. It is increasingly recognised that hybridisation events occurred during the evolutionary history of numerous species, especially during the early stages of adaptive radiation. We study the effect of hybridisation on diversification in the clownfishes, a clade of coral reef fish that diversified through an adaptive radiation process. While two species of clownfish are likely to have been described from hybrid specimens, the occurrence and effect of hybridisation on the clade diversification is yet unknown.ResultsWe generate sequences of three mitochondrial genes to complete an existing dataset of nuclear sequences and document cytonuclear discordance at a node, which shows a drastic increase of diversification rate. Then, using a tree-based jack-knife method, we identify clownfish species likely stemming from hybridisation events. Finally, we use molecular cloning and identify the putative parental species of four clownfish specimens that display the morphological characteristics of hybrids.ConclusionsOur results show that consistently with the syngameon hypothesis, hybridisation events are linked with a burst of diversification in the clownfishes. Moreover, several recently diverged clownfish lineages likely originated through hybridisation, which indicates that diversification, catalysed by hybridisation events, may still be happening.

Adaptive filtering methods for identifying cross-frequency couplings in human EEG.

Oscillations have been increasingly recognized as a core property of neural responses that contribute to spontaneous, induced, and evoked activities within and between individual neurons and neural ensembles. They are considered as a prominent mechanism for information processing within and communication between brain areas. More recently, it has been proposed that interactions between periodic components at different frequencies, known as cross-frequency couplings, may support the integration of neuronal oscillations at different temporal and spatial scales. The present study details methods based on an adaptive frequency tracking approach that improve the quantification and statistical analysis of oscillatory components and cross-frequency couplings. This approach allows for time-varying instantaneous frequency, which is particularly important when measuring phase interactions between components. We compared this adaptive approach to traditional band-pass filters in their measurement of phase-amplitude and phase-phase cross-frequency couplings. Evaluations were performed with synthetic signals and EEG data recorded from healthy humans performing an illusory contour discrimination task. First, the synthetic signals in conjunction with Monte Carlo simulations highlighted two desirable features of the proposed algorithm vs. classical filter-bank approaches: resilience to broad-band noise and oscillatory interference. Second, the analyses with real EEG signals revealed statistically more robust effects (i.e. improved sensitivity) when using an adaptive frequency tracking framework, particularly when identifying phase-amplitude couplings. This was further confirmed after generating surrogate signals from the real EEG data. Adaptive frequency tracking appears to improve the measurements of cross-frequency couplings through precise extraction of neuronal oscillations.

Adaptive Privacy Management System Design For Context-Aware Mobile Devices

While mobile technologies can provide great personalized services for mobile users, they also threaten their privacy. Such personalization-privacy paradox are particularly salient for context aware technology based mobile applications where user's behaviors, movement and habits can be associated with a consumer's personal identity. In this thesis, I studied the privacy issues in the mobile context, particularly focus on an adaptive privacy management system design for context-aware mobile devices, and explore the role of personalization and control over user's personal data. This allowed me to make multiple contributions, both theoretical and practical. In the theoretical world, I propose and prototype an adaptive Single-Sign On solution that use user's context information to protect user's private information for smartphone. To validate this solution, I first proved that user's context is a unique user identifier and context awareness technology can increase user's perceived ease of use of the system and service provider's authentication security. I then followed a design science research paradigm and implemented this solution into a mobile application called "Privacy Manager". I evaluated the utility by several focus group interviews, and overall the proposed solution fulfilled the expected function and users expressed their intentions to use this application. To better understand the personalization-privacy paradox, I built on the theoretical foundations of privacy calculus and technology acceptance model to conceptualize the theory of users' mobile privacy management. I also examined the role of personalization and control ability on my model and how these two elements interact with privacy calculus and mobile technology model. In the practical realm, this thesis contributes to the understanding of the tradeoff between the benefit of personalized services and user's privacy concerns it may cause. By pointing out new opportunities to rethink how user's context information can protect private data, it also suggests new elements for privacy related business models.

Clustering of Schistosoma mansoni mRNA sequences and analysis of the most transcribed genes: implications in metabolism and biology of different developmental stages

The study of the Schistosoma mansoni genome, one of the etiologic agents of human schistosomiasis, is essential for a better understanding of the biology and development of this parasite. In order to get an overview of all S. mansoni catalogued gene sequences, we performed a clustering analysis of the parasite mRNA sequences available in public databases. This was made using softwares PHRAP and CAP3. The consensus sequences, generated after the alignment of cluster constituent sequences, allowed the identification by database homology searches of the most expressed genes in the worm. We analyzed these genes and looked for a correlation between their high expression and parasite metabolism and biology. We observed that the majority of these genes is related to the maintenance of basic cell functions, encoding genes whose products are related to the cytoskeleton, intracellular transport and energy metabolism. Evidences are presented here that genes for aerobic energy metabolism are expressed in all the developmental stages analyzed. Some of the most expressed genes could not be identified by homology searches and may have some specific functions in the parasite.

Replacement of (R)-methadone by a double dose of (R,S)-methadone in addicts: interindividual variability of the (R)/(S) ratios and evidence of adaptive changes in methadone pharmacokinetics.

METHODS: Twenty-two patients receiving (R)-methadone maintenance treatment were switched to a double dose of (R,S)-methadone: blood samples were collected before and after the change, and the concentrations of the enantiomers were measured. In the second period, during racemic methadone treatment, important interindividual variability in the stereoselective disposition of the enantiomers of methadone was measured, with (R)/(S) ratios ranging from 0.63 to 2.40. This point should be taken into account particularly with respect to therapeutic drug monitoring of racemic methadone. RESULTS: A significant decrease P < 0.005 in the mean serum concentration/dose ratios of the active (R)-enantiomer before and after the change was measured (mean 3.97 and 3.33). CONCLUSION: Although of small amplitude (16%), this decrease confirms previously described adaptive changes in methadone pharmacokinetics during racemic methadone maintenance treatment and may necessitate, in some patients, a dose adjustment.

Clustering and hot spot detection in socio-economic spatio-temporal data

Distribution of socio-economic features in urban space is an important source of information for land and transportation planning. The metropolization phenomenon has changed the distribution of types of professions in space and has given birth to different spatial patterns that the urban planner must know in order to plan a sustainable city. Such distributions can be discovered by statistical and learning algorithms through different methods. In this paper, an unsupervised classification method and a cluster detection method are discussed and applied to analyze the socio-economic structure of Switzerland. The unsupervised classification method, based on Ward's classification and self-organized maps, is used to classify the municipalities of the country and allows to reduce a highly-dimensional input information to interpret the socio-economic landscape. The cluster detection method, the spatial scan statistics, is used in a more specific manner in order to detect hot spots of certain types of service activities. The method is applied to the distribution services in the agglomeration of Lausanne. Results show the emergence of new centralities and can be analyzed in both transportation and social terms.