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Clinical trials using adult stem cells to regenerate damaged heart tissue continue to this day(1,2), despite ongoing questions of efficacy and a lack of mechanistic understanding of the underlying biological effect(3). The rationale for these cell therapy trials is derived from animal studies that show a modest but reproducible improvement in cardiac function in models of cardiac ischaemic injury(4,5). Here we examine the mechanistic basis for cell therapy in mice after ischaemia-reperfusion injury, and find that-although heart function is enhanced-it is not associated with the production of new cardiomyocytes. Cell therapy improved heart function through an acute sterile immune response characterized by the temporal and regional induction of CCR2(+) and CX3CR1(+) macrophages. Intracardiac injection of two distinct types of adult stem cells, cells killed by freezing and thawing or a chemical inducer of the innate immune response all induced a similar regional accumulation of CCR2(+) and CX3CR1(+) macrophages, and provided functional rejuvenation to the heart after ischaemia-reperfusion injury. This selective macrophage response altered the activity of cardiac fibroblasts, reduced the extracellular matrix content in the border zone and enhanced the mechanical properties of the injured area. The functional benefit of cardiac cell therapy is thus due to an acute inflammatory-based wound-healing response that rejuvenates the infarcted area of the heart.

Since its introduction, the reward prediction error theory of dopamine has explained a wealth of empirical phenomena, providing a unifying framework for understanding the representation of reward and value in the brain(1-3). According to the now canonical theory, reward predictions are represented as a single scalar quantity, which supports learning about the expectation, or mean, of stochastic outcomes. Here we propose an account of dopamine-based reinforcement learning inspired by recent artificial intelligence research on distributional reinforcement learning(4-6). We hypothesized that the brain represents possible future rewards not as a single mean, but instead as a probability distribution, effectively representing multiple future outcomes simultaneously and in parallel. This idea implies a set of empirical predictions, which we tested using single-unit recordings from mouse ventral tegmental area. Our findings provide strong evidence for a neural realization of distributional reinforcement learning.

Analyses of single-cell recordings from mouse ventral tegmental area are consistent with a model of reinforcement learning in which the brain represents possible future rewards not as a single mean of stochastic outcomes, as in the canonical model, but instead as a probability distribution.

Colonization, speciation and extinction are dynamic processes that influence global patterns of species richness(1-6). Island biogeography theory predicts that the contribution of these processes to the accumulation of species diversity depends on the area and isolation of the island(7,8). Notably, there has been no robust global test of this prediction for islands where speciation cannot be ignored(9), because neither the appropriate data nor the analytical tools have been available. Here we address both deficiencies to reveal, for island birds, the empirical shape of the general relationships that determine how colonization, extinction and speciation rates co-vary with the area and isolation of islands. We compiled a global molecular phylogenetic dataset of birds on islands, based on the terrestrial avifaunas of 41 oceanic archipelagos worldwide (including 596 avian taxa), and applied a new analysis method to estimate the sensitivity of island-specific rates of colonization, speciation and extinction to island features (area and isolation). Our model predicts-with high explanatory power-several global relationships. We found a decline in colonization with isolation, a decline in extinction with area and an increase in speciation with area and isolation. Combining the theoretical foundations of island biogeography(7,8) with the temporal information contained in molecular phylogenies(10) proves a powerful approach to reveal the fundamental relationships that govern variation in biodiversity across the planet.

Using a global molecular phylogenetic dataset of birds on islands, the sensitivity of island-specific rates of colonization, speciation and extinction to island features (area and isolation) is estimated.