Co-evolution Mechanism Research Articles

Co-evolution mechanism of policy mix and institutional context of renewable electricity consumption in China

Co-evolution mechanism of policy mix and institutional context of renewable electricity consumption in China

Can science and technology resources co-evolve with high-tech industries? Empirical evidence from China

Based on the theories of synergetic and evolutionary economics, we build a theoretical model of the co-evolutionary mechanisms of the composite systems of science and technology resources and high-tech industries in China. According to the established index system of the composite systems, we calculated the steady-state solution of the co-evolve and drew the potential function curve of science and technology resources and high-tech industries in China during 2010–2022 using the Haken model. Our empirical results show that high-tech industries are the order parameter of co-evolve. During the sample period, there is a synergistic growth effect of two-way promotion between science and technology resources and high-tech industries in China. However, within the system, the positive feedback mechanism of science and technology resources to high-tech industries in China is not perfect, and the allocation of science and technology resources needs to be further optimized, with a low degree of system order and large room for improvement. In addition, the endowment of science and technology resources and the development degree of high-tech industries in China are different in the eight comprehensive economic zones, and their co-evolvement is different. Therefore, by combining the advantages and allocating the elements of regional science and technology resources, we can better leverage the comparative advantages of high-tech industries in China and improve the degree of co-evolve in the system.

Coevolution Mechanism of Remanufacturer–Construction Enterprise–Public in Construction and Demolition Waste Resource Utilization Projects under Green Value Co-Creation

The utilization of resources plays a crucial role in mitigating the environmental pollution issue that improper disposal of construction and demolition waste (CDW) causes. However, the slow growth of the recycled building materials market limits the development of CDW resource utilization. Green value co-creation among remanufacturers, construction enterprises, and the public in CDW resource utilization projects is an effective way to address the issue. This study, based on the theory of value co-creation, uses the evolutionary game method to construct an evolutionary game model for CDW resource utilization projects. The main conclusions are as follows: (1) When the degree of green value co-creation is 0.1 or 0.5, the remanufacturer, the construction enterprise, and the public cannot maintain a state of green value co-creation; when the degree of green value co-creation is 0.9, the remanufacturer, the construction enterprise, and the public in the CDW resource utilization project finally reach a stable state of green value co-creation. (2) When the degree of green value co-creation is 0.5, enhancing the green value co-creation willingness of the remanufacturer or the public can lead other CDW resource utilization project stakeholders to participate in green value co-creation. This study contributes to the promotion of stakeholder cooperation in CDW resource utilization projects, thus providing implications for the promotion of CDW resources.

Biased gene introgression and adaptation in the face of chloroplast capture in Aquilegia amurensis.

Chloroplast capture, a phenomenon that can occur through interspecific hybridization and introgression, is frequently invoked to explain cytonuclear discordance in plants. However, relatively few studies have documented the mechanisms of cytonuclear coevolution and its potential for driving species differentiation and possible functional differences in the context of chloroplast capture. To address this crucial question, we chose the Aquilegia genus, which is known for having minimal sterility among species, and inferred that A. amurensis captured the plastome of A. parviflora based on cytonuclear discordance and gene flow between the two species. We focused on the introgression region and its differentiation from corresponding regions in closely related species, especially its composition in a chloroplast capture scenario. We found that nuclear genes encoding cytonuclear enzyme complexes (CECs; i.e., organelle-targeted genes) of chloroplast donor species were selectively retained and displaced the original CEC genes in chloroplast-receiving species due to cytonuclear interactions during introgression. Notably, the intrinsic correlation of CEC introgression was a greater degree of evolutionary distance for these CECs between A. amurensis and A. parviflora. Terpene synthase activity genes (GO: 0010333) were overrepresented among the introgressed genes, and more than 30% of these genes were CEC genes. These findings support our observations that floral terpene release pattern is similar between A. amurensis and A. parviflora compared with A. japonica. Our study clarifies the mechanisms of cytonuclear coevolution, species differentiation and functional differences in the context of chloroplast capture and highlights the potential role of chloroplast capture in adaptation.

Multi-policy deep reinforcement learning for multi-objective multiplicity flexible job shop scheduling

This study considers the simultaneous minimization of makespan and total tardiness for the multi-objective multiplicity flexible job shop scheduling problem (MOMFJSP). A deep reinforcement learning framework employing a multi-policy proximal policy optimization algorithm (MPPPO) is developed to solve MOMFJSP. The MOMFJSP is treated as a Markov decision process, allowing an intelligent agent to make sequential decisions based on the current production status. This framework involves multiple policy networks with different objective weight vectors. Using MPPPO, these networks are optimized simultaneously to obtain a set of high-quality Pareto-optimal policies. Moreover, a fluid model is introduced to extract state features and devise composite dispatching rules as discrete actions. A multi-policy co-evolution mechanism (MPCEM) is proposed to facilitate collaborative evolution among policy networks, supported by a reward mechanism that considers the objective weights. A training algorithm based on MPPPO is designed for learning across multiple policy networks. The effectiveness and superiority of the proposed method are confirmed through comparisons with composite dispatching rules and other scheduling methods.

Phylogenomics of Tetraopes longhorn beetles unravels their evolutionary history and biogeographic origins

Tetraopes longhorn beetles are known for their resistance to milkweed plant toxins and their coevolutionary dynamics with milkweed plants (Asclepias). This association is considered a textbook example of coevolution, in which each species of Tetraopes is specialized to feed on one or a few species of Asclepias. A major challenge to investigating coevolutionary hypotheses and conducting molecular ecology studies lies in the limited understanding of the evolutionary history and biogeographical patterns of Tetraopes. By integrating genomic, morphological, paleontological, and geographical data, we present a robust phylogeny of Tetraopes and their relatives, using three inference methods with varying subsets of data, encompassing 2–12 thousand UCE loci. We elucidate the diversification patterns of Tetraopes species across major biogeographical regions and their colonization of the American continent. Our findings suggest that the genus originated in Central America approximately 21 million years ago during the Miocene and diversified from the Mid-Miocene to the Pleistocene. These events coincided with intense geological activity in Central America. Additionally, independent colonization events in North America occurred from the Late Miocene to the early Pleistocene, potentially contributing to the early diversification of the group. Our data suggest that a common ancestor of Tetraopini migrated into North America, likely facilitated by North Atlantic land bridges, while closely related tribes diverged in Asia and Europe during the Paleocene. Establishing a robust and densely sampled phylogeny of Tetraopes beetles provides a foundation for investigating micro- and macroevolutionary phenomena, including clinal variation, coevolution, and detoxification mechanisms in this ecologically important group.

Coevolutionary Diagenesis in Tight Sandstone and Shale Reservoirs within Lacustrine-Delta Systems: A Case Study from the Lianggaoshan Formation in the Eastern Sichuan Basin, Southwest China

Tight sandstone and shale oil and gas are the key targets of unconventional oil and gas exploration in the lake-delta sedimentary systems of China. Understanding the coevolutionary diagenesis of sandstone and shale reservoirs is crucial for the prediction of reservoir quality, ahead of drilling, in such systems. Thin-section description, scanning electron microscopy (SEM), X-ray diffraction (XRD), fluid inclusion analysis, porosity and permeability tests, high-pressure mercury intrusion (HPMI) measurements and nuclear magnetic resonance tests (NMR) were used to reveal the coevolutionary diagenetic mechanisms of a sandstone and shale reservoir in the Lianggaoshan Formation of the Eastern Sichuan Basin, China. The thermally mature, organic-matter-rich, dark shale of layer3 is the most important source rock within the Lianggaoshan Formation. It started to generate abundant organic acids at the early stage of mesodiagenesis and produced abundant hydrocarbons in the early Cretaceous. Porewater with high concentrations of Ca2+ and CO32− entered the sandstone reservoir from dark shale as the shale was compacted during burial. Potassium feldspar dissolution at the boundary of the sandstone was more pervasive than at the center of the sandstone. The K+ released by potassium feldspar dissolution migrated from the sandstone into mudstone. Grain-rimming chlorite coats occurred mainly in the center of the sandstone. Some silica exported from the shale was imported by the sandstone boundary and precipitated close to the shale/sandstone boundary. Some intergranular dissolution pores and intercrystal pores were formed in the shale due to dissolution during the early stages of mesodiagenesis. Chlorite coats, which precipitated during eodiagenesis, were beneficial to the protection of primary pore space in the sandstone. Calcite cement, which preferentially precipitated at the boundary of sandstone, was not conducive to reservoir development. Dissolution mainly occurred at the early stage of mesodiagenesis due to organic acids derived from the dark shale. Calcite cement could also protect some primary pores from compaction and release pore space following dissolution. The porosity of sandstone and shale was mainly controlled by the thickness of sandstone and dark shale.

Coupled dynamics of information propagation and emotion influence: Emerging emotion clusters for public health emergency messages on the Chinese Sina Microblog

A major distinction between the COVID-19 pandemic and previous pandemics is the significant role of social media platforms in shaping public adherence to non-pharmaceutical interventions and vaccine acceptance. The information propagation and individual emotion communication relevant to public health messages have been promoted in the Chinese Sina Microblog, one of the most popular social platforms in China, through two major interaction mechanisms, “commenting” and “forwarding”. Here, we develop a hybrid system of macro information propagation dynamics Susceptible-Commenting-Forwarding-Immune model coupled with the micro emotion influence dynamics Hegselmann-Krause model, termed HK-SCFI coupled system, and reveal the mechanisms of individual emotion infection using the commenting and forwarding data (including time point and content) publicly available on public opinion events in the Chinese Sina Microblog. We demonstrate how our data-driven and model-informed analysis can provide quantitative insights into the co-evolution mechanism of individual emotion during information propagation, describe the emergency of emotion clusters, and predict individual emotion evolution trends. Our study shows that multiple interactive mechanisms can facilitate the propagation of public messages and shape public sentiment through online social networks. Our modeling framework can potentially be a tool for designing effective propagation strategies during public health emergencies.

Alanine metabolism mediates energy allocation of the brown planthopper to adapt to resistant rice

IntroductionDuring the adaptation to host plant resistance, herbivorous insects faced the challenge of overcoming plant defenses while ensuring their own development and reproductive success. To achieve this, a strategic allocation of energy resources for detoxification and ecological fitness maintenance became essential. ObjectiveThis study aimed to elucidate the intricate energy allocation mechanisms involved in herbivore adaptation that are currently poorly understood. MethodsThe rice Oryza sativa and its monophagous pest, the brown planthopper (BPH), Nilaparvata lugens were used as a model system. An integrated analysis of metabolomes and transcriptomes from different BPH populations were conducted to identify the biomarkers. RNA interference of key genes and exogenous injection of key metabolites were performed to validate the function of biomarkers. ResultsWe found that alanine was one of the key biomarkers of BPH adaptation to resistant rice variety IR36. We also found that alanine flow determined the adaptation of BPH to IR36 rice. The alanine aminotransferase (ALT)-mediated alanine transfer to pyruvate was necessary and sufficient for the adaptation. This pathway may be conserved, at least to some extent, in BPH adaptation to multiple rice cultivars with different resistance genes. More importantly, ALT-mediated alanine metabolism is the foundation of downstream energy resource allocation for the adaptation. The adapted BPH population exhibited a significantly higher level of energy reserves in the fat body and ovary when fed with IR36 rice, compared to the unadapted population. This rendered the elevated detoxification in the adapted BPH and their ecological fitness recovery. ConclusionOverall, our findings demonstrated the crucial role of ALT-mediated alanine metabolism in energy allocation during the adaptation to resistant rice in BPH. This will provide novel knowledge regarding the co-evolutionary mechanisms between herbivores and their host plants.

The co-evolution mechanism of policy mixes and innovation ecosystems: a case study of the new energy vehicle industry in China

The co-evolution mechanism of policy mixes and innovation ecosystems: a case study of the new energy vehicle industry in China

The co-evolution mechanism of policy mixes and innovation ecosystems: a case study of the new energy vehicle industry in China

The co-evolution mechanism of policy mixes and innovation ecosystems: a case study of the new energy vehicle industry in China

Nature: Methyl-salicylate-mediated plant airborne defense and mechanism of interaction and coevolution between plant viruses, vector insects, and host plants

The discipline of plant immunity has developed rapidly in the 21st century and is marked by substantial progress toward understanding the molecular mechanisms of plant immune recognition and the pathogenicity of microorganisms. Here, we highlight the recent advances in plant immunity studies made by Yule Liu and his team at Tsinghua University in Beijing, China.

Genetic variants associated with hantavirus infection in a reservoir host are related to regulation of inflammation and immune surveillance

The immunogenetics of wildlife populations influence the epidemiology and evolutionary dynamic of the host-pathogen system. Profiling immune gene diversity present in wildlife may be especially important for those species that, while not at risk of disease or extinction themselves, are host to diseases that are a threat to humans, other wildlife, or livestock. Hantaviruses (genus: Orthohantavirus) are globally distributed zoonotic RNA viruses with pathogenic strains carried by a diverse group of rodent hosts. The marsh rice rat (Oryzomys palustris) is the reservoir host of Orthohantavirus bayoui, a hantavirus that causes fatal cases of hantavirus cardiopulmonary syndrome in humans. We performed a genome wide association study (GWAS) using the rice rat “immunome” (i.e., all exons related to the immune response) to identify genetic variants associated with infection status in wild-caught rice rats naturally infected with their endemic strain of hantavirus. First, we created an annotated reference genome using 10× Chromium Linked Reads sequencing technology. This reference genome was used to create custom baits which were then used to target enrich prepared rice rat libraries (n = 128) and isolate their immunomes prior to sequencing. Top SNPs in the association test were present in four genes (Socs5, Eprs, Mrc1, and Il1f8) which have not been previously implicated in hantavirus infections. However, these genes correspond with other loci or pathways with established importance in hantavirus susceptibility or infection tolerance in reservoir hosts: the JAK/STAT, MHC, and NFκB. These results serve as informative markers for future exploration and highlight the importance of immune pathways that repeatedly emerge across hantavirus systems. Our work aids in creating cross-species comparisons for better understanding mechanisms of genetic susceptibility and host-pathogen coevolution in hantavirus systems.

Synergistic Responses of Tibetan Sheep Rumen Microbiota, Metabolites, and the Host to the Plateau Environment.

Plateau adaptation in animals involves genetic mechanisms as well as coevolutionary mechanisms of the microbiota and metabolome of the animal. Therefore, the characteristics of the rumen microbiome and metabolome, transcriptome, and serum metabolome of Tibetan sheep at different altitudes (4500 m, 3500 m, and 2500 m) were analyzed. The results showed that the rumen differential metabolites at 3500 m and 4500 m were mainly enriched in amino acid metabolism, lipid metabolism, and carbohydrate metabolism, and there was a significant correlation with microbiota. The differentially expressed genes and metabolites at middle and high altitudes were coenriched in asthma, arachidonic acid metabolism, and butanoate and propanoate metabolism. In addition, the serum differential metabolites at 3500 m and 4500 m were mainly enriched in amino acid metabolism, lipid metabolism, and metabolism of xenobiotics by cytochrome P450, and they were also related to microbiota. Further analysis revealed that rumen metabolites accounted for 7.65% of serum metabolites. These common metabolites were mainly enriched in metabolic pathways and were significantly correlated with host genes (p < 0.05). This study found that microbiota, metabolites, and epithelial genes were coenriched in pathways related to lipid metabolism, energy metabolism, and immune metabolism, which may be involved in the regulation of Tibetan sheep adaptation to plateau environmental changes.

Exploring the Synergy Between Technological Innovation and Business Model InnovationTaking NIO as an Example

In the age of the digital economy, many enterprises use new technologies to empower their enterprises, and their technological innovation is closely related to their business models. In the era of digital economy, how companies achieve business models and technological innovation is an important issue. The support and driving business model innovation of new technologies, and business model innovation can guide technological innovation, thereby achieving the coordinated development of the two and jointly promoting the rapid development of the enterprise. Based on the longitudinal analysis of the case of NIO, a typical enterprise, this paper analyzes the dynamic co-evolution mechanism of technology innovation and business model innovation, and establishes the relevant model. By summarizing the results of the case analysis, this paper concludes that technological innovation promotes business model innovation, and business model innovation drives technological innovation, and the co-dynamic evolution mechanism of the two has three main modes: one-way, interactive, and integration.

Cost-efficient security-aware scheduling for dependent tasks with endpoint contention in edge computing

Edge computing empowers latency-sensitive and computation-intensive applications composed of multiple dependent tasks of mobile devices to execute on nearby edge servers. Despite the advantages, intermediate data across edge servers faces external security threats. Moreover, most existing works on task scheduling are designed on the idealized system model where communications are performed in parallel, they neglect the endpoint contention problem during data transmission, and thereby can hardly meet the user-specified deadline. To tackle these issues, this paper studies the endpoint contention-aware scheduling problem for dependent tasks to minimize the execution cost under the security and deadline constraints in the edge computing environment. We propose a heuristic algorithm named CSSDE that first distributes the deadline to each task and sorts tasks according to probabilistic endpoint contention-aware upward rank, and then allocates bandwidth resources and computation resources to intermediate data and tasks, respectively, finally performs rescheduling to improve the initial scheduling scheme. In addition, based on CSSDE and genetic algorithm, we present a metaheuristic algorithm called C-GA that considers the impact of task ordering and incorporates a multi-population co-evolution mechanism to enhance population diversity and avoid falling into local optimum for a better scheduling scheme. Simulation experiments are conducted on synthetic and real-world applications. The results demonstrate that our proposed algorithms achieve a higher success rate, and C-GA reduces the execution cost by 43.92, 40.52, and 8.56 percent compared with SCAS-E, ELSH, and CSSDE, respectively.

Antibiotic hyper-resistance in a class I aminoacyl-tRNA synthetase with altered active site signature motif

Antibiotics target key biological processes that include protein synthesis. Bacteria respond by developing resistance, which increases rapidly due to antibiotics overuse. Mupirocin, a clinically used natural antibiotic, inhibits isoleucyl-tRNA synthetase (IleRS), an enzyme that links isoleucine to its tRNAIle for protein synthesis. Two IleRSs, mupirocin-sensitive IleRS1 and resistant IleRS2, coexist in bacteria. The latter may also be found in resistant Staphylococcus aureus clinical isolates. Here, we describe the structural basis of mupirocin resistance and unravel a mechanism of hyper-resistance evolved by some IleRS2 proteins. We surprisingly find that an up to 103-fold increase in resistance originates from alteration of the HIGH motif, a signature motif of the class I aminoacyl-tRNA synthetases to which IleRSs belong. The structural analysis demonstrates how an altered HIGH motif could be adopted in IleRS2 but not IleRS1, providing insight into an elegant mechanism for coevolution of the key catalytic motif and associated antibiotic resistance.

Asymmetry of individual activity promotes cooperation in the spatial prisoner's dilemma game.

We consider an aspiration-based asymmetric individual activity co-evolutionary prisoner's dilemma game model on the square lattice. In detail, each player has an individual weight that evolves with its strategy. We introduce an asymmetric setting that only μ fractions of players in the network chosen to be active can update their individual weights according to whether their payoffs satisfy their aspirations. Therefore, our individual weights can be regarded as a type of intrinsic motivation satisfaction. Through Monte Carlo simulations on the square lattice, our co-evolution mechanism has been proven to significantly promote cooperation. Furthermore, we find that the moderate μ leads to the highest cooperation level. By dividing the players into four types according to their strategies and weights, we prove that the inversion of the dominant relationship between cooperators and defectors with low weights leads to a conversion cycle of the four types of players, which enhances spatial reciprocity to promote cooperation. Moderate μ results in an appropriate number of low-weight players in this cycle. Interestingly, our results show that higher weight heterogeneity does not lead to higher levels of cooperation, which is contrary to the intuition formed in the previous work.

Discarding–Recovering and Co-Evolution Mechanisms Based Evolutionary Algorithm for Hyperspectral Feature Selection

With the improvement of spectral resolution, the redundant information in the hyperspectral imaging (HSI) datasets brings computational, analytical, and storage complexities. Feature selection is a combinatorial optimization problem, which selects a subset of feasible features to reduce the dimensionality of data and decrease the noise information. In recent years, the evolutionary algorithm (EA) has been widely used in feature selection, but the diversity of agents is lacking in the population, which leads to premature convergence. In this paper, a feature selection method based on discarding–recovering and co-evolution mechanisms is proposed with the aim of obtaining an effective feature combination in HSI datasets. The feature discarding mechanism is introduced to remove redundant information by roughly filtering the feature space. To further enhance the agents’ diversity, the reliable information interaction is also designed into the co-evolution mechanism, and if detects the event of stagnation, a subset of discarded features will be recovered using adaptive weights. Experimental results demonstrate that the proposed method performs well on three public datasets, achieving an overall accuracy of 92.07%, 92.36%, and 98.01%, respectively, and obtaining the number of selected features between 15% and 25% of the total.

Convergent resistance to GABA receptor neurotoxins through plant-insect coevolution.

The molecular mechanisms of coevolution between plants and insects remain elusive. GABA receptors are targets of many neurotoxic terpenoids, which represent the most diverse array of natural products known. Over deep evolutionary time, as plant terpene synthases diversified in plants, so did plant terpenoid defence repertoires. Here we show that herbivorous insects and their predators evolved convergent amino acid changing substitutions in duplicated copies of the Resistance to dieldrin (Rdl) gene that encodes the GABA receptor, and that the evolution of duplicated Rdl and terpenoid-resistant GABA receptors is associated with the diversification of moths and butterflies. These same substitutions also evolved in pests exposed to synthetic insecticides that target the GABA receptor. We used in vivo genome editing in Drosophila melanogaster to evaluate the fitness effects of each putative resistance mutation and found that pleiotropy both facilitates and constrains the evolution of GABA receptor resistance. The same genetic changes that confer resistance to terpenoids across 300 Myr of insect evolution have re-evolved in response to synthetic analogues over one human lifespan.