Reputation-based synergy and discounting mechanism promotes cooperation

A good group reputation often facilitates more efficient synergistic teamwork in production activities. Here we translate this simple motivation into a reputation-based synergy and discounting mechanism in the public goods game. Specifically, the reputation type of a group, either good or bad determined by a reputation threshold, modifies the nonlinear payoff structure described by a unified reputation impact factor. Results show that this reputation-based incentive mechanism could effectively promote cooperation compared with linear payoffs, despite the coexistence of synergy and discounting effects. Notably, the complicated interactions between reputation impact and reputation threshold result in a sharp phase transition from full cooperation to full defection. We also find that the presence of a few discounting groups could increase the average payoffs of cooperators, leading to an interesting phenomenon that when the reputation threshold is raised, the gap between the average payoffs of cooperations and defectors increases while the overall payoff decreases. Our work provides important insights into facilitating cooperation in social groups

Physical Information Neural Networks for Solving High-index Differential-algebraic Equation Systems Based on Radau Methods

As is well known, differential algebraic equations (DAEs), which are able to describe dynamic changes and underlying constraints, have been widely applied in engineering fields such as fluid dynamics, multi-body dynamics, mechanical systems and control theory. In practical physical modeling within these domains, the systems often generate high-index DAEs. Classical implicit numerical methods typically result in varying order reduction of numerical accuracy when solving high-index systems.~Recently, the physics-informed neural network (PINN) has gained attention for solving DAE systems. However, it faces challenges like the inability to directly solve high-index systems, lower predictive accuracy, and weaker generalization capabilities. In this paper, we propose a PINN computational framework, combined Radau IIA numerical method with a neural network structure via the attention mechanisms, to directly solve high-index DAEs. Furthermore, we employ a domain decomposition strategy to enhance solution accuracy. We conduct numerical experiments with two classical high-index systems as illustrative examples, investigating how different orders of the Radau IIA method affect the accuracy of neural network solutions. The experimental results demonstrate that the PINN based on a 5th-order Radau IIA method achieves the highest level of system accuracy. Specifically, the absolute errors for all differential variables remains as low as $10^{-6}$, and the absolute errors for algebraic variables is maintained at $10^{-5}$, surpassing the results found in existing literature. Therefore, our method exhibits excellent computational accuracy and strong generalization capabilities, providing a feasible approach for the high-precision solution of larger-scale DAEs with higher indices or challenging high-dimensional partial differential algebraic equation systems

NLRP3 Is Involved in the Maintenance of Cerebral Pericytes

Pericytes play a central role in regulating the structure and function of capillaries in the brain. However, molecular mechanisms that drive pericyte proliferation and differentiation are unclear. In our study, we immunostained NACHT, LRR and PYD domains-containing protein 3 (NLRP3)-deficient and wild-type littermate mice and observed that NLRP3 deficiency reduced platelet-derived growth factor receptor β (PDGFRβ)-positive pericytes and collagen type IV immunoreactive vasculature in the brain. In Western blot analysis, PDGFRβ and CD13 proteins in isolated cerebral microvessels from the NLRP3-deficient mouse brain were decreased. We further treated cultured pericytes with NLRP3 inhibitor, MCC950, and demonstrated that NLRP3 inhibition attenuated cell proliferation but did not induce apoptosis. NLRP3 inhibition also decreased protein levels of PDGFRβ and CD13 in cultured pericytes. On the contrary, treatments with IL-1β, the major product of NLRP3-contained inflammasome, increased protein levels of PDGFRβ, and CD13 in cultured cells. The alteration of PDGFRβ and CD13 protein levels were correlated with the phosphorylation of AKT. Inhibition of AKT reduced both protein markers and abolished the effect of IL-1β activation in cultured pericytes. Thus, NLRP3 activation might be essential to maintain pericytes in the healthy brain through phosphorylating AKT. The potential adverse effects on the cerebral vascular pericytes should be considered in clinical therapies with NLRP3 inhibitors

DREAMSeq: An Improved Method for Analyzing Differentially Expressed Genes in RNA-seq Data

RNA sequencing (RNA-seq) has become a widely used technology for analyzing global gene-expression changes during certain biological processes. It is generally acknowledged that RNA-seq data displays equidispersion and overdispersion characteristics; therefore, most RNA-seq analysis methods were developed based on a negative binomial model capable of capturing both equidispersed and overdispersed data. In this study, we reported that in addition to equidispersion and overdispersion, RNA-seq data also displays underdispersion characteristics that cannot be adequately captured by general RNA-seq analysis methods. Based on a double Poisson model capable of capturing all data characteristics, we developed a new RNA-seq analysis method (DREAMSeq). Comparison of DREAMSeq with five other frequently used RNA-seq analysis methods using simulated datasets showed that its performance was comparable to or exceeded that of other methods in terms of type I error rate, statistical power, receiver operating characteristics (ROC) curve, area under the ROC curve, precision-recall curve, and the ability to detect the number of differentially expressed genes, especially in situations involving underdispersion. These results were validated by quantitative real-time polymerase chain reaction using a real Foxtail dataset. Our findings demonstrated DREAMSeq as a reliable, robust, and powerful new method for RNA-seq data mining. The DREAMSeq R package is available at http://tanglab.hebtu.edu.cn/tanglab/Home/DREAMSeq

Improved Data Transmission Scheme of Network Coding Based on Access Point Optimization in VANET

VANET is a hot spot of intelligent transportation researches. For vehicle users, the file sharing and content distribution through roadside access points (AP) as well as the vehicular ad hoc networks (VANET) have been an important complement to that cellular network. So the AP deployment is one of the key issues to improve the communication performance of VANET. In this paper, an access point optimization method is proposed based on particle swarm optimization algorithm. The transmission performances of the routing protocol with random linear network coding before and after the access point optimization are analyzed. The simulation results show the optimization model greatly affects the VANET transmission performances based on network coding, and it can enhance the delivery rate by 25% and 14% and reduce the average delay of transmission by 38% and 33%

Proxy-RLHF: Decoupling Generation and Alignment in Large Language Model with Proxy

Reinforcement Learning from Human Feedback (RLHF) is the prevailing approach to ensure Large Language Models (LLMs) align with human values. However, existing RLHF methods require a high computational cost, one main reason being that RLHF assigns both the generation and alignment tasks to the LLM simultaneously. In this paper, we introduce Proxy-RLHF, which decouples the generation and alignment processes of LLMs, achieving alignment with human values at a much lower computational cost. We start with a novel Markov Decision Process (MDP) designed for the alignment process and employ Reinforcement Learning (RL) to train a streamlined proxy model that oversees the token generation of the LLM, without altering the LLM itself. Experiments show that our method achieves a comparable level of alignment with only 1\% of the training parameters of other methods

Variety-driven rhizosphere microbiome bestows differential salt tolerance to alfalfa for coping with salinity stress

Soil salinization is a global environmental issue and a significant abiotic stress that threatens crop production. Root-associated rhizosphere microbiota play a pivotal role in enhancing plant tolerance to abiotic stresses. However, limited information is available concerning the specific variations in rhizosphere microbiota driven by different plant genotypes (varieties) in response to varying levels of salinity stress. In this study, we compared the growth performance of three alfalfa varieties with varying salt tolerance levels in soils with different degrees of salinization. High-throughput 16S rRNA and ITS sequencing were employed to analyze the rhizosphere microbial communities. Undoubtedly, the increasing salinity significantly inhibited alfalfa growth and reduced rhizosphere microbial diversity. However, intriguingly, salt-tolerant varieties exhibited relatively lower susceptibility to salinity, maintaining more stable rhizosphere bacterial community structure, whereas the reverse was observed for salt-sensitive varieties. Bacillus emerged as the dominant species in alfalfa's adaptation to salinity stress, constituting 21.20% of the shared bacterial genera among the three varieties. The higher abundance of Bacillus, Ensifer, and Pseudomonas in the rhizosphere of salt-tolerant alfalfa varieties is crucial in determining their elevated salt tolerance. As salinity levels increased, salt-sensitive varieties gradually accumulated a substantial population of pathogenic fungi, such as Fusarium and Rhizoctonia. Furthermore, rhizosphere bacteria of salt-tolerant varieties exhibited increased activity in various metabolic pathways, including biosynthesis of secondary metabolites, carbon metabolism, and biosynthesis of amino acids. It is suggested that salt-tolerant alfalfa varieties can provide more carbon sources to the rhizosphere, enriching more effective plant growth-promoting bacteria (PGPB) such as Pseudomonas to mitigate salinity stress. In conclusion, our results highlight the variety-mediated enrichment of rhizosphere microbiota in response to salinity stress, confirming that the high-abundance enrichment of specific dominant rhizosphere microbes and their vital roles play a significant role in conferring high salt adaptability to these varieties

The BR signaling pathway regulates primary root development and drought stress response by suppressing the expression of PLT1 and PLT2 in Arabidopsis thaliana

IntroductionWith the warming global climate, drought stress has become an important abiotic stress factor limiting plant growth and crop yield. As the most rapidly drought-sensing organs of plants, roots undergo a series of changes to enhance their ability to absorb water, but the molecular mechanism is unclear.Results and methodsIn this study, we found that PLT1 and PLT2, two important transcription factors of root development in Arabidopsis thaliana, are involved in the plant response to drought and are inhibited by BR signaling. PLT1- and PLT2-overexpressing plants showed greater drought tolerance than wild-type plants. Furthermore, we found that BZR1 could bind to the promoter of PLT1 and inhibit its transcriptional activity in vitro and in vivo. PLT1 and PLT2 were regulated by BR signaling in root development and PLT2 could partially rescue the drought sensitivity of bes1-D. In addition, RNA-seq data analysis showed that BR-regulated root genes and PLT1/2 target genes were also regulated by drought; for example, CIPK3, RCI2A, PCaP1, PIP1;5, ERF61 were downregulated by drought and PLT1/2 but upregulated by BR treatment; AAP4, WRKY60, and AT5G19970 were downregulated by PLT1/2 but upregulated by drought and BR treatment; and RGL2 was upregulated by drought and PLT1/2 but downregulated by BR treatment.DiscussionOur findings not only reveal the mechanism by which BR signaling coordinates root growth and drought tolerance by suppressing the expression of PLT1 and PLT2 but also elucidates the relationship between drought and root development. The current study thus provides an important theoretical basis for the improvement of crop yield under drought conditions