Obesity is a metabolic disorder related to improper control of energy uptake and expenditure, which results in excessive accumulation of body fat. Initial insights into the genetic pathways that regulate energy metabolism have been provided by a discrete number of obesity-related genes that have been identified in mammals. Here, we report the identification of the adipose (adp) gene, the mutation of which causes obesity in Drosophila. Loss of adp activity promotes increased fat storage, which extends the lifespan of mutant flies under starvation conditions. By contrast, adp gain-of-function causes a specific reduction of the fat body in Drosophila. adp encodes an evolutionarily conserved WD40/tetratricopeptide-repeat-domain protein that is likely to represent an intermediate in a novel signalling pathway.

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The emergence of network-movements since 2011 has opened the debate around the way in which social media and networked practices make possible innovative forms of collective identity. We briefly review the literature on social movements and 'collective identity', and show the tension between different positions stressing either organization or culture, the personal or the collective, aggregative or networking logics. We argue that the 15M (indignados) network-movement in Spain demands conceptual and methodological innovations. Its rapid emergence, endurance, diversity, multifaceted development and adaptive capacity, posit numerous theoretical and methodological challenges. We show how the use of structural and dynamic analysis of interaction networks (in combination with qualitative data) is a valuable tool to track the shape and change of what we term the 'systemic dimension' of collective identities in network-movements. In particular, we introduce a novel method for synchrony detection in Facebook activity to identify the distributed, yet integrated, coordinated activity behind collective identities. Applying this analytical strategy to the 15M movement, we show how it displays a specific form of systemic collective identity we call 'multitudinous identity', characterized by social transversality and internal heterogeneity, as well as a transient and distributed leadership driven by action initiatives. Our approach attends to the role of distributed interaction and transient leadership at a mesoscale level of organizational dynamics, which may contribute to contemporary discussions of collective identity in network-movements.

In recent years, researchers in social cognition have found the “perceptual crossing paradigm” to be both a theoretical and practical advance toward meeting particular challenges. This paradigm has been used to analyze the type of interactive processes that emerge in minimal interactions and it has allowed progress toward understanding of the principles of social cognition processes. In this paper, we analyze whether some critical aspects of these interactions could not have been observed by previous studies. We consider alternative indicators that could complete, or even lead us to rethink, the current interpretation of the results obtained from both experimental and simulated modeling in the fields of social interactions and minimal perceptual crossing. In particular, we discuss the possibility that previous experiments have been analytically constrained to a short-term dynamic type of player response. Additionally, we propose the possibility of considering these experiments from a more suitable framework based on the use and analysis of long-range correlations and fractal dynamics. We will also reveal evidence supporting the idea that social interactions are deployed along many scales of activity. Specifically, we propose that the fractal structure of the interactions could be a more adequate framework to understand the type of social interaction patterns generated in a social engagement.

Kinetic Ising models are powerful tools for studying the non-equilibrium dynamics of complex systems. As their behavior is not tractable for large networks, many mean-field methods have been proposed for their analysis, each based on unique assumptions about the system’s temporal evolution. This disparity of approaches makes it challenging to systematically advance mean-field methods beyond previous contributions. Here, we propose a unifying framework for mean-field theories of asymmetric kinetic Ising systems from an information geometry perspective. The framework is built on Plefka expansions of a system around a simplified model obtained by an orthogonal projection to a sub-manifold of tractable probability distributions. This view not only unifies previous methods but also allows us to develop novel methods that, in contrast with traditional approaches, preserve the system’s correlations. We show that these new methods can outperform previous ones in predicting and assessing network properties near maximally fluctuating regimes.

Despite the increase of both dynamic and embodied/situated approaches in cognitive science, there is still little research on how coordination dynamics under a closed sensorimotor loop might induce qualitatively different patterns of neural oscillations compared to those found in isolated systems. We take as a departure point the Haken-Kelso-Bunz (HKB) model, a generic model for dynamic coordination between two oscillatory components, which has proven useful for a vast range of applications in cognitive science and whose dynamical properties are well understood. In order to explore the properties of this model under closed sensorimotor conditions we present what we call the situated HKB model: a robotic model that performs a gradient climbing task and whose “brain” is modeled by the HKB equation. We solve the differential equations that define the agent-environment coupling for increasing values of the agent's sensitivity (sensor gain), finding different behavioral strategies. These results are compared with two different models: a decoupled HKB with no sensory input and a passively-coupled HKB that is also decoupled but receives a structured input generated by a situated agent. We can precisely quantify and qualitatively describe how the properties of the system, when studied in coupled conditions, radically change in a manner that cannot be deduced from the decoupled HKB models alone. We also present the notion of neurodynamic signature as the dynamic pattern that correlates with a specific behavior and we show how only a situated agent can display this signature compared to an agent that simply receives the exact same sensory input. To our knowledge, this is the first analytical solution of the HKB equation in a sensorimotor loop and qualitative and quantitative analytic comparison of spatially coupled vs. decoupled oscillatory controllers. Finally, we discuss the limitations and possible generalization of our model to contemporary neuroscience and philosophy of mind.

The objective of this work is to better analyse and understand social self-organization in the context of social media and political activism. More specifically, we centre our analysis in the presence of fractal scaling in the form of 1/f noise in different Twitter communication networks related to the Spanish 15M movement. We show how quantitative indexes of brown, white and pink noise correlate with qualitatively different forms of social coordination of protests: rigidly organized protests (brown noise), reactive-spontaneous protests (white noise) and complex genuinely self-organized protests (pink noise). In addition, pink noise processes present correlations that reach much further in time, maintaining a dynamical coherence that last several days, and also show a balance between mean distance and clustering coefficient within the interaction network.

The capacity to integrate information is a prominent feature of biological and cognitive systems. Integrated Information Theory (IIT) provides a mathematical approach to quantify the level of integration in a system, yet its computational cost generally precludes its applications beyond relatively small models. In consequence, it is not yet well understood how integration scales up with the size of a system or with different temporal scales of activity, nor how a system maintains its integration as its interacts with its environment. Here, we show for the first time how measures of information integration scale when systems become very large. Using kinetic Ising models and mean-field approximations from statistical mechanics, we show that information integration diverges in the thermodynamic limit at certain critical points. Moreover, by comparing different divergent tendencies of blocks of a system at these critical points, we delimit the boundary between an integrated unit and its environment. Finally, we present a model that adaptively maintains its integration despite changes in its environment by generating a critical surface where its integrity is preserved. We argue that the exploration of integrated information for these limit cases helps in addressing a variety of poorly understood questions about the organization of biological, neural, and cognitive systems. * sci@maguilera.net; Also at ISAAC Lab,