Punishment and reputation based partners-switching promotes cooperation in social networks

To investigate the cooperation dynamics caused by coevolution of game strategy and social contacts, we propose a behavioral punishment and reputation based partners-switching mechanism, in which individuals are allowed to sever unwanted partnerships and establish new ones with next-nearest neighbors having high reputations. Simulation results show that cooperation is significantly promoted under the proposed mechanism. Under greater temptation to defect or in denser networks, social partners changing needs to be adequately frequent to support the spread of cooperative behavior. For a given average degree ${\left \langle k \right \rangle}$ or temptation to defect b, a critical value for time scale ratio W can be observed, above which cooperators occupy the whole population. Our results show that the structural dynamics facilitates the emergence of an underlying heterogeneous network, which provides a favorable network topology for cooperation to prevail under strategy dynamics

Impact of contact rate on epidemic spreading in complex networks

Contact reduction is an effective strategy to mitigate the spreading of epidemic. However, the existing reaction–diffusion equations for infectious disease are unable to characterize this effect. Thus, we here propose an extended susceptible-infected-recovered model by incorporating contact rate into the standard SIR model, and concentrate on investigating its impact on epidemic transmission. We analytically derive the epidemic thresholds on homogeneous and heterogeneous networks, respectively. The effects of contact rate on spreading speed, scale and outbreak threshold are explored on ER and SF networks. Simulations results show that epidemic dissemination is significantly mitigated when contact rate is reduced. Importantly, epidemic spreads faster on heterogeneous networks while broader on homogeneous networks, and the outbreak thresholds of the former are smaller

Behavioral observability and reputational-preference–based rewarding mechanism promotes cooperation in spatial social dilemmas

To explore the incentive mechanisms of cooperation, inspired by preference for reputation in indirect reciprocity and the influence of behavioral observability on fitness, we present a new rewarding mechanism by incorporating these two impact factors into the evaluation of fitness in the spatial prisoner's dilemma game (PDG), under which a dynamically changing reward is established for cooperative neighbors whose reputation is higher than the average score of all neighbors. Simulation results reveal that the proposed rewarding mechanism favors the evolution of cooperation, under the joint effects of behavioral observability and reputational preference, cooperators can gradually agglomerate and form close clusters to defend the invasion of defectors. Moreover, we have investigated the characteristic snapshots and strategy transitions during the evolutionary process, which further validate the above outcome

Dynamical Analysis of an Improved Bidirectional Immunization SIR Model in Complex Network

In order to investigate the impact of two immunization strategies—vaccination targeting susceptible individuals to reduce their infection rate and clinical medical interventions targeting infected individuals to enhance their recovery rate—on the spread of infectious diseases in complex networks, this study proposes a bilinear SIR infectious disease model that considers bidirectional immunization. By analyzing the conditions for the existence of endemic equilibrium points, we derive the basic reproduction numbers and outbreak thresholds for both homogeneous and heterogeneous networks. The epidemic model is then reconstructed and extensively analyzed using continuous-time Markov chain (CTMC) methods. This analysis includes the investigation of transition probabilities, transition rate matrices, steady-state distributions, and the transition probability matrix based on the embedded chain. In numerical simulations, a notable concordance exists between the outcomes of CTMC and mean-field (MF) simulations, thereby substantiating the efficacy of the CTMC model. Moreover, the CTMC-based model adeptly captures the inherent stochastic fluctuation in the disease transmission, which is consistent with the mathematical properties of Markov chains. We further analyze the relationship between the system’s steady-state infection density and the immunization rate through MCS. The results suggest that the infection density decreases with an increase in the immunization rate among susceptible individuals. The current research results will enhance our understanding of infectious disease transmission patterns in real-world scenarios, providing valuable theoretical insights for the development of epidemic prevention and control strategies

Reputational preference-based payoff punishment promotes cooperation in spatial social dilemmas

To explore the incentive mechanisms of cooperation in social dilemmas. Motivated by preference for reputation in indirect reciprocity, we propose a reputational preference-based payoff punishment mechanism, under which an individual is punished if his reputation is lower than the average one of direct neighbors and his current game strategy is defection. The cost of punishment is shared by the immediate neighbors. Simulation results show that in spatial prisoner’s dilemma game and snowdrift game, the punishment mechanism reduces the fitness of both cooperators and defectors in the micro-perspective, whereas it significantly promotes the evolution of cooperation from the macro view. Furthermore, it is easier for cooperation to emerge and sustain in snowdrift game, and compared to prisoner’s dilemma game, within the most range of model parameters, the system is in the coexistence state of cooperators and defectors

Arabidopsis proteome and the mass spectral assay library

Arabidopsis is an important model organism and the first plant with its genome completely sequenced. Knowledge from studying this species has either direct or indirect applications for agriculture and human health. Quantitative proteomics by data-independent acquisition mass spectrometry (SWATH/DIA-MS) was recently developed and is considered as a high-throughput, massively parallel targeted approach for accurate proteome quantification. In this approach, a high-quality and comprehensive spectral library is a prerequisite. Here, we generated an expression atlas of 10 organs of Arabidopsis and created a library consisting of 15,514 protein groups, 187,265 unique peptide sequences, and 278,278 precursors. The identified protein groups correspond to ~56.5% of the predicted proteome. Further proteogenomics analysis identified 28 novel proteins. We applied DIA-MS using this library to quantify the effect of abscisic acid on Arabidopsis. We were able to recover 8,793 protein groups of which 1,787 were differentially expressed. MS data are available via ProteomeXchange with identifier PXD012708 and PXD012710 for data-dependent acquisition and PXD014032 for DIA analyses.ISSN:2052-446

Piglets cloned from induced pluripotent stem cells

Embryonic stem (ES) cells are powerful tools for generating genetically modified animals that can assist in advancing our knowledge of mammalian physiology and disease. Pigs provide outstanding models of human genetic diseases due to the striking similarities to human anatomy, physiology and genetics, but progress with porcine genetic engineering has been hampered by the lack of germline-competent pig ES cells. To overcome this limitation, genetically modified pigs have been produced using genetically modified somatic cells and nuclear transfer (NT). Yet, somatic cells exhibit limited proliferative capacity and have an extremely low frequency of homologous recombination compared to ES cells. Hence, only a few knockout pig models have been reported thus far using standard gene-targeting approaches