Liu Yang

Graphical AbstractTo mechanistically understand the dynamics of complex ecosystems, Yize Chen et al. employ symbolic regression (SR), a machine learning method that automatically reverse-engineers both model structure and parameters from temporal data. SR randomly assembles candidate models, computes the model fitness, and employs mutation and crossover to build better ones. The Pareto front reflects the trade-off between complexity and fitness of candidate models. More details can be found in article number 1900069 by Yize Chen et al.

The human gut microbiota is a very complex and dynamic ecosystem that plays a crucial role in health and well‐being. Inferring microbial community structure and dynamics directly from time‐resolved metagenomics data is key to understanding the community ecology and predicting its temporal behavior. Many methods have been proposed to perform the inference. Yet, as we point out in this review, there are several pitfalls along the way. Indeed, the uninformative temporal measurements and the compositional nature of the relative abundance data raise serious challenges in inference. Moreover, the inference results can be largely distorted when only focusing on highly abundant species by ignoring or grouping low‐abundance species. Finally, the implicit assumptions in various regularization methods may not reflect reality. Those issues have to be seriously considered in ecological modeling of human gut microbiota.

Understanding the dynamics of complex ecosystems is a necessary step to maintain and control them. Yet, reverse-engineering ecological dynamics remains challenging largely due to the very broad class of dynamics that ecosystems may take. Here, this challenge is tackled through symbolic regression, a machine learning method that automatically reverse-engineers both the model structure and parameters from temporal data. How combining symbolic regression with a “dictionary” of possible ecological functional responses opens the door to correctly reverse-engineering ecosystem dynamics, even in the case of poorly informative data, is shown. This strategy is validated using both synthetic and experimental data, and it is found that this strategy is promising for the systematic modeling of complex ecological systems.

Behavioral psychology is increasingly important in relationship marketing. As key factors of emotional interactions between buyer and seller, psychological contracts and opportunistic behaviors play a critical role in interorganizational relationships which are based on personal relationships of boundary spanners and top management. Most of the existing research mainly focus on positive performance of cooperation but ignoring the dark side of relationships. This study introduces the psychological contract into the exploration of why formal contracts cannot completely avoid opportunistic behaviors. It mainly investigates whether psychological contracts in relationships can reduce the occurrence of opportunistic behaviors. The results show that psychological contract has a significant positive effect on the relationship quality, and negatively affect opportunistic behavior through trust and commitment. The positive relationship between psychological contracts and relationship quality is moderated by dependence. This study enriches and expands the domestic and foreign research on psychological contracts and opportunistic behaviors in relationship marketing.

The present work aims to efficiently carry out life-death education for college students, improve their psychological problems, and reduce suicide accidents by combining LDE with Dance Movement Therapy. DMT is a psychosomatic cross therapy that treats mental or physical diseases through dance or improvisation. Firstly, this paper introduces LDE and DMT and designs the activities of DMT intervention. Secondly, the relationship between DMT and LDE is analyzed. Finally, a questionnaire survey is conducted on the research objects. The research objects are divided into the experimental group receiving DMT intervention, the control group participating in sports dance courses, and the benchmark group. The research data show no significant difference in interpersonal skills and emotional psychology among the three groups. The DMT intervention group has substantial changes in other factors except for the conflict control ability before and after the intervention. In addition, the questionnaire survey results after the intervention demonstrate that the DMT intervention group gets a significantly higher score in the interpersonal relationship, interpersonal relationship building ability, moderate rejection ability, self-disclosure ability, and emotional support ability. Therefore, DMT intervention positively impacts college students’ interpersonal relationships. After the sports dance course, there are differences in the total score of interpersonal ties and scores of interpersonal relationships building ability, self-disclosure ability, and emotional support ability in the control group, but with no significant difference in moderate rejection ability and conflict control ability. Therefore, compared with traditional psychotherapy methods, the DMT method reported here is conducive to releasing pressure and alleviating physical and mental anxiety. The research content provides new ideas for psychological education in colleges and universities and contributes to improving college students’ suicidal tendencies and helping college students grow up healthily.