Heterogeneity within tumour cell populations is commonly observed in most cancers. However, its impact on metastatic dissemination, one of the primary determinants of the disease prognosis, remains poorly understood. Working with a simplified numerical model of tumour spheroids, we investigated the impact of mechanical heterogeneity on the onset of tumour invasion into surrounding tissues. Our work establishes a positive link between tumour heterogeneity and metastatic dissemination, and recapitulates a number of invasion patterns identified in vivo, such as multicellular finger-like protrusions. Two complementary mechanisms are at play in heterogeneous tumours. A small proportion of stronger cells are able to initiate and lead the escape of cells, while collective effects in the bulk of the tumour provide the coordination required to sustain the invasive process through multicellular streaming. This suggests that the multicellular dynamics observed during metastasis is a generic feature of mechanically heterogeneous cell populations and might rely on a limited and generic set of attributes.
There is an important dissonance in recent studies of children's work between the global efforts to eradicate abusive forms of child labour on the one hand and, on the other hand, local settings where children's work plays an important role in social reproduction, socialization and skill acquisition. This research explores the reasons for this dissonance by eliding both the global perspectives of children's rights and the local realities of children's daily geographies. By closing the gap between global knowledge about children (from above) and children's knowledge and agency in their own environments (from below), we seek to present a relational account of children's work within the context of their daily geographies. We draw on data collected with child workers in Tijuana, Mexico to demonstrate the complex role that children's work often plays in the daily geographies of young people.
Although multi-agent reinforcement learning can tackle systems of strategically interacting entities, it currently fails in scalability and lacks rigorous convergence guarantees. Crucially, learning in multi-agent systems can become intractable due to the explosion in the size of the state-action space as the number of agents increases. In this paper, we propose a method for computing closed-loop optimal policies in multi-agent systems that scales independently of the number of agents. This allows us to show, for the first time, successful convergence to optimal behaviour in systems with an unbounded number of interacting adaptive learners. Studying the asymptotic regime of N-player stochastic games, we devise a learning protocol that is guaranteed to converge to equilibrium policies even when the number of agents is extremely large. Our method is model-free and completely decentralised so that each agent need only observe its local state information and its realised rewards. We validate these theoretical results by showing convergence to Nash-equilibrium policies in applications from economics and control theory with thousands of strategically interacting agents.
The issue of child labor continues to challenge thinking on the nature of work, play, schooling and apprenticeship. New wisdom from some contemporary academic writing places children closer to the center of our understanding of consumption, production and reproduction, and at the heart of inequities generated by globalization. Child labor comes in many forms and intersects with local life and global processes in a myriad of ways. The child laborers in this study work as ‘volunteer’ checkout packers in Tijuana supermarkets. By highlighting aspects of their complex daily lives, this article develops new ways of thinking about children's work socially and spatially, while acknowledging the global contexts of this work.
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