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The proportion of the integrated converters using PSC （power synchronization control） in power systems is increasing year by year， but there is a lack of in-depth research on the transient synchronization stability. Therefore， the transient synchronization stability mechanism of integrated grid-forming converters using PSC is analyzed， and measures to improve the transient stability of the converter are proposed. First， the transient stability model of the grid-forming converters with PSC during grid fault is established. Subsequently， based on the phase diagram curve （PDC） method， the destabilization mechanism of the converters and the effect of voltage coupling on the transient synchronous stability are investigated. Moreover， a control strategy to improve the transient synchronous stability by dynamically adjusting the active power command value is proposed based on the effect of voltage coupling. Finally， the electromagnetic transient simulation model of an equivalent system in a city is constructed by using PSCAD/EMTDC， which verifies the correctness of the transient stabilization mechanism of the converter-connected power system and the effectiveness of the stabilization enhancement measures under different fault conditions.

Poria cocos, a well-known traditional Chinese edible-medicinal mushroom, has shown many beneficial health effects. In this study, we investigated the anti-obesity and hyperlipidemia effect of carboxymethyl pachymaran (CMP) in high-fat diet mice, with a focus on gut microbiota and their metabolites. Results demonstrated that CMP notably decreased fat weights and serum lipid, improved glucose tolerance, and reduced lipid droplet level in hepatic tissue. CMP markedly reduced the ratio between Firmicutes and Bacteroidetes, decreased the abundances of Dubosiella newyorkensis and Faecealibaculum rodentium, but increased the abundances of Akkermansia muciniphila and Bifidobacterium pseudolongum. In addition, CMP significantly promoted the production of butyric acid (BA) while decreased the levels of hepatic bile acids. Association analysis showed that physio-biochemical traits were negatively associated with A. muciniphila and BA, whereas showed a positive correlation to F. rodentium and differential bile acids. Together, CMP can alleviate obesity and related complications by modulating gut microbiota and metabolites.

Tuber brumale and Tuber indicum (Pezizomycetes) are two edible black truffles establishing ectomycorrhizal symbiosis with trees and shrubs. T. brumale is ubiquitous in Europe, and T. indicum is mainly found in China. Here, we present the draft genome sequences of T. brumale and T. indicum.

Mycorrhizal fungi are mutualists that play crucial roles in nutrient acquisition in terrestrial ecosystems. Mycorrhizal symbioses arose repeatedly across multiple lineages of Mucoromycotina, Ascomycota, and Basidiomycota. Considerable variation exists in the capacity of mycorrhizal fungi to acquire carbon from soil organic matter. Here, we present a combined analysis of 135 fungal genomes from 73 saprotrophic, endophytic and pathogenic species, and 62 mycorrhizal species, including 29 new mycorrhizal genomes. This study samples ecologically dominant fungal guilds for which there were previously no symbiotic genomes available, including ectomycorrhizal Russulales, Thelephorales and Cantharellales. Our analyses show that transitions from saprotrophy to symbiosis involve (1) widespread losses of degrading enzymes acting on lignin and cellulose, (2) co-option of genes present in saprotrophic ancestors to fulfill new symbiotic functions, (3) diversification of novel, lineage-specific symbiosis-induced genes, (4) proliferation of transposable elements and (5) divergent genetic innovations underlying the convergent origins of the ectomycorrhizal guild.

Edible-medicinal fungi have shown many beneficial health effects. Cordyceps guangdongensis is a novel edible-medicinal fungus in China. In this study, we investigated the effects of ethanolic extract from C. guangdongensis fruiting bodies (CGE), and comprehensively dissected the effects of CGE on fat accumulation, dyslipidemia, gut microbial composition, and short chain fatty acids (SCFAs). Results showed that CGE markedly inhibited body weight gain, decreased fat accumulation in various tissues, and significantly improved glucose tolerance and hyperlipidemia. CGE notably restored high-fat diet induced microbial shift from Bacteroidetes to Firmicutes, and markedly increased the abundances of beneficial gut microbiota (such as Akkermansia muciniphila and Bifidobacterium animalis). Interestingly, CGE also significantly promoted butyric acid (BA) production. Pairwise correlation analysis found that A. muciniphila, B. animals and BA were negatively associated with several fat-related parameters and blood lipid indicators. Together, our results suggested that CGE represented a potential novel supplement to treat obesity and related complications.

The quick， accurate and instantaneous evaluation of power limit of transmission section is of great significance to safe， stable and economic operation of power grid. The paper proposes an adaptive method based on a hybrid supervised learning algorithm. Through numerous simulations and analysis of historic operation modes， characteristic extraction， training， and regular updating of AI model， the correct matching relationship between grid operation status and power limit is established for fast， accurate and instantaneous power limit evaluation of the transmission section. The proposed method employs multiple supervised learning forecast algorithms， including deep neural networks， support vector regression， gradient boosting decision tree， and random forest. The hybrid models for power limit forecasting can make best of the advantages of the algorithms. The effectiveness of the method is verified on a 500-bus transmission model with real operation characteristics.

As the planning and dispatching of new types of power systems become increasingly complicated， dynamic and dubious， it is very challenging to lay down operating mode for power grid that satisfies both security and economic constraints. This process typically requires intensive manual tuning and simulation calculations to search for feasible solutions in a high-dimensional action space that fulfills the requirements on safety and economy of the grid in ground state and fault conditions. Therefore， an approach based on multi-DRL-agent architecture is proposed to describe the problem as a Markov decision process （MDP）. The approach automatically adjusts different types of controllable grid resources by training centralized and distributed multi-DRL-agent to control the power of transmission lines， thus complying with various safety indicators of grid operation. The method is proved to be effective in a power grid model.