Determination of higenamine in common natural spices by ultrahigh-performance liquid chromatography-tandem mass spectrometry.

Higenamine (HG) is a ß2 receptor agonist and was explicitly added to the Prohibited List of the World Anti-Doping Agency in 2017. This compound is prohibited in both in- and out-of-competition athletes and falls under the category of nonthreshold substances. Because of HG presence in numerous plants, as evidenced by a growing body of research data, an exception was made for HG in the TD2017MRPL document, in which adverse analytical findings (AAFs) were not reported if the urinary HG concentration was less than 10 ng/mL. In this study, a comprehensive and systematic analysis of the HG content in five batches of samples from each of the 48 natural spices selected for this investigation was conducted using UPLC-MS/MS technology. Method validation was carried out in accordance with the ICH Analytical Procedures and Methods Validation for Drugs and Biologics Guidance, and the experimental results demonstrated that the method provided appropriate sensitivity, precision, stability, linearity, and accuracy. HG was detected for the first time in Houttuynia cordata, Zingiber officinale, Cinnamomum cassia, Stevia rebaudiana, Piper nigrum, Siraitia grosuenorii, Platycodon grandiflorus, and Myristica fragrans. Furthermore, the content of HG was found to vary significantly among the different plant parts of Nelumbo nucifera, such as rhizomes, leaves, seeds, and plumules. This paper provides systematic and comprehensive data to support the safe use of spices in athletes' diets, thereby reducing the risk of food-sourced doping violations.

Co-fermentation of titanium-flocculated-sludge with food waste towards simultaneous water purification and resource recovery.

Recovery of resources from domestic sewage and food waste has always been an international-thorny problem. Titanium-based flocculation can achieve high-efficient destabilization, quick concentration and separation of organic matter from sewage to sludge. This study proposed co-fermentation of the titanium-flocculated sludge (Ti-loaded sludge) and food waste towards resource recovery by converting organic matter to value-added volatile fatty acids (VFAs) and inorganic matter to struvite and TiO2 nanoparticles. When Ti-loaded sludge and food waste were co-fermented at a mass ratio of 3:1, the VFAs yield reached 3725.2 mg-COD/L (VFAs/SCOD 91.0%), which was more than 4 times higher than the case of the sludge alone. The 48-day semicontinuous co-fermentation demonstrated stable long-term operation, yielding VFAs at 2529.0 mg-COD/L (VFAs/SCOD 89.8%) and achieving a high CODVFAs/NNH4 of 58.9. Food waste provided sufficient organic substrate, enriching plenty of acid-producing fermentation bacteria (such as Prevotella 7 about 21.0% and Bacteroides about 9.4%). Moreover, metagenomic sequencing analysis evidenced the significant increase of the relative gene abundance corresponding to enzymes in pathways, such as extracellular hydrolysis, substrates metabolism, and VFAs biosynthesis. After fermentation, the precious element P (≥ 99.0%) and extra-added element Ti (≥99.0%) retained in fermented residues, without releasing to VFAs supernatant, which facilitated the direct re-use of VFAs as resource. Through simple and commonly used calcination and acid leaching methodologies, 80.9% of element P and 82.1% of element Ti could be successfully recovered as struvite and TiO2 nanoparticles, respectively. This research provides a strategy for the co-utilization of domestic sludge and food waste, which can realize both reduction of sludge and recovery of resources.

Aspiration dynamics generate robust predictions in heterogeneous populations.

Update rules, which describe how individuals adjust their behavior over time, affect the outcome of social interactions. Theoretical studies have shown that evolutionary outcomes are sensitive to model details when update rules are imitation-based but are robust when update rules are self-evaluation based. However, studies of self-evaluation based rules have focused on homogeneous population structures where each individual has the same number of neighbors. Here, we consider heterogeneous population structures represented by weighted networks. Under weak selection, we analytically derive the condition for strategy success, which coincides with the classical condition of risk-dominance. This condition holds for all weighted networks and distributions of aspiration levels, and for individualized ways of self-evaluation. Our findings recover previous results as special cases and demonstrate the universality of the robustness property under self-evaluation based rules. Our work thus sheds light on the intrinsic difference between evolutionary dynamics under self-evaluation based and imitation-based update rules.

Impacts of preferences on the emergence of cooperation.

Behavior decision making, where individuals can efficiently express their preferences for all options, has a great impact on cooperation. Hereby, we institute a minimal model in well-mixed populations where whether and how to sanction defectors are decided by cooperators via different decision-making mechanisms. The results illustrate that whether cooperation can outbreak depends on the cooperators' preferences for sanction and complying with the electoral outcome. We highlight the role of individuals' preferences in the emergence of cooperation and show that there exists an intermediate degree of the cooperators' preference for sanction at which the cooperators' preference for complying with the electoral outcome has a negligible impact on cooperation. We point out whether conformity facilitates the emergence of cooperation depends on the cooperators' preference for sanction. We find, compared with individual decision making, whether collective decision making is more conducive to promoting cooperation crucially depends on cooperators' preferences as well as the consensus required for employing sanction.

Evolution of cooperation under punishment.

Punishment has been considered as an effective mechanism for promoting and sustaining cooperation. In most existing models, punishment always comes as a third strategy alongside cooperation and defection, and it is commonly assumed to be executed based on individual decision rules rather than collective decision rules. Differently from previous works, we employ a democratic procedure by which cooperators cast votes independently and simultaneously for whether to impose punishment on defectors, and we establish a relationship between the cooperators' willingness to punish defectors (WTPD) and whether the punishment is inflicted on defectors. The results illustrate that the population can evolve to full cooperation under consensual punishment. It is noteworthy that, compared with autonomous punishment, whether consensual punishment is more in favor of cooperation crucially depends on the minimum number of votes required for punishment execution as well as the cooperators' WTPD. Our findings highlight the importance of collective decision making in the evolution of cooperation and may provide a mathematical framework for explaining the prevalence of democracy in modern societies.

Insurance optimizes complex interactive and cooperative behaviors in public goods games.

Global cooperation is urgently needed to prevent risks of world-wide extreme events and disasters for sustainable development. In human societies, however, there exists bias toward interacting with partners with similar characteristics, but not contributing globally. We study how complex interactive behaviors evolve under risks through proposing a threshold public goods game model. In the model, individuals either play games with participants who own the same phenotype, or contribute to the collective target of global public goods. We further introduce an insurance compensation mechanism into the model to probe the evolution of global cooperation. It is found that the introduction of the insurance remarkably promotes the emergence of global cooperative behavior and inhibits the tendency to play games only with individuals of the same phenotype. Besides, contrary to models without insurance, global cooperation is strengthened with the increase of imitation intensities. In addition, high risk and high threshold are in favor of global cooperation.

Evolving synergetic interactions.

Cooperators forgo their own interests to benefit others. This reduces their fitness and thus cooperators are not likely to spread based on natural selection. Nonetheless, cooperation is widespread on every level of biological organization ranging from bacterial communities to human society. Mathematical models can help to explain under which circumstances cooperation evolves. Evolutionary game theory is a powerful mathematical tool to depict the interactions between cooperators and defectors. Classical models typically involve either pairwise interactions between individuals or a linear superposition of these interactions. For interactions within groups, however, synergetic effects may arise: their outcome is not just the sum of its parts. This is because the payoffs via a single group interaction can be different from the sum of any collection of two-player interactions. Assuming that all interactions start from pairs, how can such synergetic multiplayer games emerge from simpler pairwise interactions? Here, we present a mathematical model that captures the transition from pairwise interactions to synergetic multiplayer ones. We assume that different social groups have different breaking rates. We show that non-uniform breaking rates do foster the emergence of synergy, even though individuals always interact in pairs. Our work sheds new light on the mechanisms underlying such synergetic interactions.

Evolutionary dynamics of general group interactions in structured populations.

The evolution of populations is influenced by many factors, and the simple classical models have been developed in a number of important ways. Both population structure and multiplayer interactions have been shown to significantly affect the evolution of important properties, such as the level of cooperation or of aggressive behavior. Here we combine these two key factors and develop the evolutionary dynamics of general group interactions in structured populations represented by regular graphs. The traditional linear and threshold public goods games are adopted as models to address the dynamics. We show that for linear group interactions, population structure can favor the evolution of cooperation compared to the well-mixed case, and we see that the more neighbors there are, the harder it is for cooperators to persist in structured populations. We further show that threshold group interactions could lead to the emergence of cooperation even in well-mixed populations. Here population structure sometimes inhibits cooperation for the threshold public goods game, where depending on the benefit to cost ratio, the outcomes are bistability or a monomorphic population of defectors or cooperators. Our results suggest, counterintuitively, that structured populations are not always beneficial for the evolution of cooperation for nonlinear group interactions.

Aspiration dynamics in structured population acts as if in a well-mixed one.

Understanding the evolution of human interactive behaviors is important. Recent experimental results suggest that human cooperation in spatial structured population is not enhanced as predicted in previous works, when payoff-dependent imitation updating rules are used. This constraint opens up an avenue to shed light on how humans update their strategies in real life. Studies via simulations show that, instead of comparison rules, self-evaluation driven updating rules may explain why spatial structure does not alter the evolutionary outcome. Though inspiring, there is a lack of theoretical result to show the existence of such evolutionary updating rule. Here we study the aspiration dynamics, and show that it does not alter the evolutionary outcome in various population structures. Under weak selection, by analytical approximation, we find that the favored strategy in regular graphs is invariant. Further, we show that this is because the criterion under which a strategy is favored is the same as that of a well-mixed population. By simulation, we show that this holds for random networks. Although how humans update their strategies is an open question to be studied, our results provide a theoretical foundation of the updating rules that may capture the real human updating rules.

Aspiration dynamics of multi-player games in finite populations.

On studying strategy update rules in the framework of evolutionary game theory, one can differentiate between imitation processes and aspiration-driven dynamics. In the former case, individuals imitate the strategy of a more successful peer. In the latter case, individuals adjust their strategies based on a comparison of their pay-offs from the evolutionary game to a value they aspire, called the level of aspiration. Unlike imitation processes of pairwise comparison, aspiration-driven updates do not require additional information about the strategic environment and can thus be interpreted as being more spontaneous. Recent work has mainly focused on understanding how aspiration dynamics alter the evolutionary outcome in structured populations. However, the baseline case for understanding strategy selection is the well-mixed population case, which is still lacking sufficient understanding. We explore how aspiration-driven strategy-update dynamics under imperfect rationality influence the average abundance of a strategy in multi-player evolutionary games with two strategies. We analytically derive a condition under which a strategy is more abundant than the other in the weak selection limiting case. This approach has a long-standing history in evolutionary games and is mostly applied for its mathematical approachability. Hence, we also explore strong selection numerically, which shows that our weak selection condition is a robust predictor of the average abundance of a strategy. The condition turns out to differ from that of a wide class of imitation dynamics, as long as the game is not dyadic. Therefore, a strategy favoured under imitation dynamics can be disfavoured under aspiration dynamics. This does not require any population structure, and thus highlights the intrinsic difference between imitation and aspiration dynamics.

Evolution of global cooperation driven by risks.

Globalization facilitates our communication with each other, while it magnifies problems such as overharvesting of natural resources and human-induced climate change. Thus people all over the world are involved in a global social dilemma which calls for worldwide cooperation to reduce the risks of these extreme events and disasters. A collective target (threshold) is required to prevent such events. Everyone may lose their wealth once their total individual contributions fail to reach the threshold. To this end, we establish a model of threshold public goods games in a group-structured population and investigate its evolutionary process. We study multilevel public goods games with defectors, local cooperators, and global cooperators and are primarily concerned with how the global cooperative behavior evolves. We find that, compared with the standard public goods games, the strategy of global cooperation accounts for a bigger proportion in the stationary distribution of threshold public goods games. On the other hand, the fixation time of the global cooperation strategy is greatly shortened with increase of the probability of disaster striking. Therefore, global risks induced by the threshold can effectively promote global cooperation in environmental investment and the reduction of greenhouse gas emissions.