Transitions between homophilic and heterophilic modes of cooperation

Cooperation is ubiquitous in biological and social systems. Previous studies revealed that a preference toward similar appearance promotes cooperation, a phenomenon called tag-mediated cooperation or communitarian cooperation. This effect is enhanced when a spatial structure is incorporated, because space allows agents sharing an identical tag to regroup to form locally cooperative clusters. In spatially distributed settings, one can also consider migration of organisms, which has a potential to further promote evolution of cooperation by facilitating spatial clustering. However, it has not yet been considered in spatial tag-mediated cooperation models. Here we show, using computer simulations of a spatial model of evolutionary games with organismal migration, that tag-based segregation and homophilic cooperation arise for a wide range of parameters. In the meantime, our results also show another evolutionarily stable outcome, where a high level of heterophilic cooperation is maintained in spatially well-mixed patterns. We found that these two different forms of tag-mediated cooperation appear alternately as the parameter for temptation to defect is increased.Comment: 16 pages, 7 figure

Adaptive long-range migration promotes cooperation under tempting conditions

Migration is a fundamental trait in humans and animals. Recent studies investigated the effect of migration on the evolution of cooperation, showing that contingent migration favors cooperation in spatial structures. In those studies, only local migration to immediate neighbors was considered, while long-range migration has not been considered yet, partly because the long-range migration has been generally regarded as harmful for cooperation as it would bring the population to a well-mixed state that favors defection. Here, we studied the effects of adaptive long-range migration on the evolution of cooperation through agent-based simulations of a spatial Prisoner's Dilemma game where individuals can jump to a farther site if they are surrounded by more defectors. Our results show that adaptive long-range migration strongly promotes cooperation, especially under conditions where the temptation to defect is considerably high. These findings demonstrate the significance of adaptive long-range migration for the evolution of cooperation.Comment: 7 pages, 9 figure

Collective Chasing Behavior between Cooperators and Defectors in the Spatial Prisoner’s Dilemma

Cooperation is one of the essential factors for all biological organisms in major evolutionary transitions. Recent studies have investigated the effect of migration for the evolution of cooperation. However, little is known about whether and how an individuals’ cooperativeness coevolves with mobility. One possibility is that mobility enhances cooperation by enabling cooperators to escape from defectors and form clusters; the other possibility is that mobility inhibits cooperation by helping the defectors to catch and exploit the groups of cooperators. In this study we investigate the coevolutionary dynamics by using the prisoner’s dilemma game model on a lattice structure. The computer simulations demonstrate that natural selection maintains cooperation in the form of evolutionary chasing between the cooperators and defectors. First, cooperative groups grow and collectively move in the same direction. Then, mutant defectors emerge and invade the cooperative groups, after which the defectors exploit the cooperators. Then other cooperative groups emerge due to mutation and the cycle is repeated. Here, it is worth noting that, as a result of natural selection, the mobility evolves towards directional migration, but not to random or completely fixed migration. Furthermore, with directional migration, the rate of global population extinction is lower when compared with other cases without the evolution of mobility (i.e., when mobility is preset to random or fixed). These findings illustrate the coevolutionary dynamics of cooperation and mobility through the directional chasing between cooperators and defectors

Possible Existence of Lysosome-Like Organella within Mitochondria and Its Role in Mitochondrial Quality Control

The accumulation of unhealthy mitochondria results in mitochondrial dysfunction, which has been implicated in aging, cancer, and a variety of degenerative diseases. However, the mechanism by which mitochondrial quality is regulated remains unclear. Here, we show that Mieap, a novel p53-inducible protein, induces intramitochondrial lysosome-like organella that plays a critical role in mitochondrial quality control. Mieap expression is directly regulated by p53 and is frequently lost in human cancer as result of DNA methylation. Mieap dramatically induces the accumulation of lysosomal proteins within mitochondria and mitochondrial acidic condition without destroying the mitochondrial structure (designated MALM, for Mieap-induced accumulation of lysosome-like organelles within mitochondria) in response to mitochondrial damage. MALM was not related to canonical autophagy. MALM is involved in the degradation of oxidized mitochondrial proteins, leading to increased ATP synthesis and decreased reactive oxygen species generation. These results suggest that Mieap induces intramitochondrial lysosome-like organella that plays a critical role in mitochondrial quality control by eliminating oxidized mitochondrial proteins. Cancer cells might accumulate unhealthy mitochondria due to p53 mutations and/or Mieap methylation, representing a potential cause of the Warburg effect

Program Slicing on VHDL Descriptions and Its Applications

We propose applying program slicing to hardware description language VHDL; program slicing is a technique developed in the software engineering field, and it can extract all the sentences which have a certain dependence to a given sentence, and it can be utilized for analysis and modification of programs. Program slicing for hardware description languages should have a variety of applications in VLSI CAD, such as functional verification, test generation, designer assistance, and design synthesis. We show several examples of program slicing on VHDL descriptions, and address a list of related issues. 1 Introduction Hardware description languages (HDLs) are now being widely used as a tool for total system description, from low-level circuit descriptions to high-level abstract and conceptual descriptions. As design resources in the form of HDL descriptions are going to be accumulated, verification, maintenance and reuse of HDL descriptions will become an important issue. We propose apply..

(a) Typical snapshots showing rightward evolution.

<p>Note that in this case, the time axis is vertical. Also see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0067702#pone.0067702.s003" target="_blank">Video S2</a>. (b) Dynamics of the number of individuals with entropy (explained later) in the evolution to the right.</p

Parameters in basic settings.

<p>Parameters in basic settings.</p

(a) Typical snapshots of the downward evolution in Fig.

<p><b>2(b).</b> Blue represents cooperators and red represents defectors. If there are less than 100 of each type, each color (blue or red) is reduced gradually. If there are two types of individuals in the same site, the colors are mixed. The green lines indicate the movement of cooperators, and yellow lines indicate the movement of defectors. The numbers next to each group are labels. The box located at the top-right space in each square indicates the generation, number of cooperators, and number of defectors. After cooperative clusters emerge and move downward, they are chased and exploited by defectors. However, other cooperative clusters emerge, and then these cycles are repeated. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0067702#pone.0067702.s002" target="_blank">Video S1</a> shows a series of this evolution. (b) Dynamics of the number of individuals with entropy (explained later). Blue signifies cooperators and red signifies defectors.</p

Entropy from the 1000th to 2000th generations (averaged over 1000 simulation runs, excluding the runs resulting in extinction) as a function of the benefit-to-cost ratio of cooperation in the three models.

<p>Entropy from the 1000th to 2000th generations (averaged over 1000 simulation runs, excluding the runs resulting in extinction) as a function of the benefit-to-cost ratio of cooperation in the three models.</p