Cooperative behavior that increases the fitness of others at a cost to oneself can be promoted by natural selection only in the presence of an additional mechanism. One such mechanism is based on population structure, which can lead to clustering of cooperating agents. Recently, the focus has turned to complex dynamical population structures such as social networks, where the nodes represent individuals and links represent social relationships. We investigate how the dynamics of a social network can change the level of cooperation in the network. Individuals either update their strategies by imitating their partners or adjust their social ties. For the dynamics of the network structure, a random link is selected and breaks with a probability determined by the adjacent individuals. Once it is broken, a new one is established. This linking dynamics can be conveniently characterized by a Markov chain in the configuration space of an ever-changing network of interacting agents. Our model can be analytically solved provided the dynamics of links proceeds much faster than the dynamics of strategies. This leads to a simple rule for the evolution of cooperation: The more fragile links between cooperating players and non-cooperating players are (or the more robust links between cooperators are), the more likely cooperation prevails. Our approach may pave the way for analytically investigating coevolution of strategy and structure.

Photoperiodic flowering is one of the most important pathways to govern flowering in rice (Oryza sativa), in which Heading date 1 (Hd1), an ortholog of the Arabidopsis CONSTANS gene, encodes a pivotal regulator. Hd1 promotes flowering under short-day conditions (SD) but represses flowering under long-day conditions (LD) by regulating the expression of Heading date 3a (Hd3a), the FLOWERING LOCUS T (FT) ortholog in rice. However, the molecular mechanism of how Hd1 changes its regulatory activity in response to day length remains largely unknown. In this study, we demonstrated that the repression of flowering in LD by Hd1 is dependent on the transcription factor DAYS TO HEADING 8 (DTH8). Loss of DTH8 function results in the activation of Hd3a by Hd1, leading to early flowering. We found that Hd1 directly interacts with DTH8 and that the formation of the DTH8-Hd1 complex is necessary for the transcriptional repression of Hd3a by Hd1 in LD, implicating that the switch of Hd1 function is mediated by DTH8 in LD rather than in SD. Furthermore, we revealed that DTH8 associates with the Hd3a promoter to modulate the level of H3K27 trimethylation (H3K27me3) at the Hd3a locus. In the presence of the DTH8-Hd1 complex, the H3K27me3 level was increased at Hd3a, whereas loss of DTH8 function resulted in decreased H3K27me3 level at Hd3a. Taken together, our findings indicate that, in response to day length, DTH8 plays a critical role in mediating the transcriptional regulation of Hd3a by Hd1 through the DTH8-Hd1 module to shape epigenetic modifications in photoperiodic flowering.

Discovery and optimization of new catalysts can be potentially accelerated by efficient data analysis using machine-learning (ML). In this paper, we record the process of searching for additives in the electrochemical deposition of Cu catalysts for CO2 reduction (CO2RR) using ML, which includes three iterative cycles: “experimental test; ML analysis; prediction and redesign”. Cu catalysts are known for CO2RR to obtain a range of products including C1 (CO, HCOOH, CH4, CH3OH) and C2+ (C2H4, C2H6, C2H5OH, C3H7OH). Subtle changes in morphology and surface structure of the catalysts caused by additives in catalyst preparation can lead to dramatic shifts in CO2RR selectivity. After several ML cycles, we obtained catalysts selective for CO, HCOOH, and C2+ products. This catalyst discovery process highlights the potential of ML to accelerate material development by efficiently extracting information from a limited number of experimental data.

The conventional cancer stem cell (CSC) theory indicates a hierarchy of CSCs and non-stem cancer cells (NSCCs), that is, CSCs can differentiate into NSCCs but not vice versa. However, an alternative paradigm of CSC theory with reversible cell plasticity among cancer cells has received much attention very recently. Here we present a generalized multi-phenotypic cancer model by integrating cell plasticity with the conventional hierarchical structure of cancer cells. We prove that under very weak assumption, the nonlinear dynamics of multi-phenotypic proportions in our model has only one stable steady state and no stable limit cycle. This result theoretically explains the phenotypic equilibrium phenomena reported in various cancer cell lines. Furthermore, according to the transient analysis of our model, it is found that cancer cell plasticity plays an essential role in maintaining the phenotypic diversity in cancer especially during the transient dynamics. Two biological examples with experimental data show that the phenotypic conversions from NCSSs to CSCs greatly contribute to the transient growth of CSCs proportion shortly after the drastic reduction of it. In particular, an interesting overshooting phenomenon of CSCs proportion arises in three-phenotypic example. Our work may pave the way for modeling and analyzing the multi-phenotypic cell population dynamics with cell plasticity.

Machine learning is used to study growth of a metal-organic framework (MOF) in a high-dimensional synthetic space. Neural networks for image processing also provide tools for automatically measuring thickness and lateral size of MOF nanoplates to provide quantitative data for further analysis. Relationships among different quantities in these synthetic endeavors were searched and evaluated with state-of-the-art mathematical tools. This works highlights new opportunities in using machine learning to expedite materials development and provides insight into their synthesis process.