Nitrogen(N)deposition is a significant aspect of global change and poses a threat to terrestrial biodiversity.The impact of plant-soil microbe relationships to N deposition has recently attracted considerable attentio...Nitrogen(N)deposition is a significant aspect of global change and poses a threat to terrestrial biodiversity.The impact of plant-soil microbe relationships to N deposition has recently attracted considerable attention.Soil microorganisms have been proven to provide nutrients for specific plant growth,especially in nutrient-poor desert steppe ecosystems.However,the effects of N deposition on plant-soil microbial community interactions in such ecosystems remain poorly understood.To investigate these effects,we conducted a 6-year N-addition field experiment in a Stipa breviflora Griseb.desert steppe in Inner Mongolia Autonomous Region,China.Four N treatment levels(N0,N30,N50,and N100,corresponding to 0,30,50,and 100 kg N/(hm2•a),respectively)were applied to simulate atmospheric N deposition.The results showed that N deposition did not significantly affect the aboveground biomass of desert steppe plants.N deposition did not significantly reduce the alfa-diversity of plant and microbial communities in the desert steppe,and low and mediate N additions(N30 and N50)had a promoting effect on them.The variation pattern of plant Shannon index was consistent with that of the soil bacterial Chao1 index.N deposition significantly affected the beta-diversity of plants and soil bacteria,but did not significantly affect fungal communities.In conclusion,N deposition led to co-evolution between desert steppe plants and soil bacterial communities,while fungal communities exhibited strong stability and did not undergo significant changes.These findings help clarify atmospheric N deposition effects on the ecological health and function of the desert steppe.展开更多
The quality of fermented vegetables is fundamentally driven by the microbiome.Although sequencing technologies have revealed patterns of microbial diversity in fermented vegetables across diverse geographic regions,pr...The quality of fermented vegetables is fundamentally driven by the microbiome.Although sequencing technologies have revealed patterns of microbial diversity in fermented vegetables across diverse geographic regions,production practices,and fermentation periods,the understanding of microbial community composition and interaction dynamics remains incomplete.Furthermore,achieving precise control over the fermentation process is still challenging.This review examines the current state of microbial succession patterns in kimchi,paocai,suansun fermented mustard and cucumber,emphasizing critical challenges in microbial control and identifying key factors influencing community dynamics,including synergistic and competitive interactions.It also presents emerging technologies in microbial spoilage prevention,aiming to enhance microbiome-informed process control.Additionally,the review assesses metabolic pathways and sensory characteristics of fermented vegetables and highlights health risks associated with compounds like sodium nitrite,biogenic amines,and harmful microorganisms.The integration of synthetic functional microbial communities is discussed as a promising approach to improve fermentation quality.Finally,the potential for digital tools such as machine learning and industrial robotics to standardize production processes and improve quality control is addressed,highlighting future directions and practical implications for the industry.Overall,these insights support a foundation for interdisciplinary research and sustainable development in the fermented vegetable industry.展开更多
The distance dynamics model is excellent tool for uncovering the community structure of a complex network. However, one issue that must be addressed by this model is its very long computation time in large-scale netwo...The distance dynamics model is excellent tool for uncovering the community structure of a complex network. However, one issue that must be addressed by this model is its very long computation time in large-scale networks. To identify the community structure of a large-scale network with high speed and high quality, in this paper, we propose a fast community detection algorithm, the F-Attractor, which is based on the distance dynamics model. The main contributions of the F-Attractor are as follows. First, we propose the use of two prejudgment rules from two different perspectives： node and edge. Based on these two rules, we develop a strategy of internal edge prejudgment for predicting the internal edges of the network. Internal edge prejudgment can reduce the number of edges and their neighbors that participate in the distance dynamics model. Second, we introduce a triangle distance to further enhance the speed of the interaction process in the distance dynamics model. This triangle distance uses two known distances to measure a third distance without any extra computation. We combine the above techniques to improve the distance dynamics model and then describe the community detection process of the F-Attractor. The results of an extensive series of experiments demonstrate that the F-Attractor offers high-speed community detection and high partition quality.展开更多
基金the National Natural Science Foundation of China(31860136,31560156)the Basic Scientific Research Service Fee Project of Colleges and Universities of Inner Mongolia Autonomous Regionthe Graduate Scientific Research Innovation Project of Inner Mongolia Autonomous Region(B20210158Z).
文摘Nitrogen(N)deposition is a significant aspect of global change and poses a threat to terrestrial biodiversity.The impact of plant-soil microbe relationships to N deposition has recently attracted considerable attention.Soil microorganisms have been proven to provide nutrients for specific plant growth,especially in nutrient-poor desert steppe ecosystems.However,the effects of N deposition on plant-soil microbial community interactions in such ecosystems remain poorly understood.To investigate these effects,we conducted a 6-year N-addition field experiment in a Stipa breviflora Griseb.desert steppe in Inner Mongolia Autonomous Region,China.Four N treatment levels(N0,N30,N50,and N100,corresponding to 0,30,50,and 100 kg N/(hm2•a),respectively)were applied to simulate atmospheric N deposition.The results showed that N deposition did not significantly affect the aboveground biomass of desert steppe plants.N deposition did not significantly reduce the alfa-diversity of plant and microbial communities in the desert steppe,and low and mediate N additions(N30 and N50)had a promoting effect on them.The variation pattern of plant Shannon index was consistent with that of the soil bacterial Chao1 index.N deposition significantly affected the beta-diversity of plants and soil bacteria,but did not significantly affect fungal communities.In conclusion,N deposition led to co-evolution between desert steppe plants and soil bacterial communities,while fungal communities exhibited strong stability and did not undergo significant changes.These findings help clarify atmospheric N deposition effects on the ecological health and function of the desert steppe.
基金financially sponsored by National Natural science Foundation of china(U23A20258)Yunnan Fundamental Research Projects(202301As070014).
文摘The quality of fermented vegetables is fundamentally driven by the microbiome.Although sequencing technologies have revealed patterns of microbial diversity in fermented vegetables across diverse geographic regions,production practices,and fermentation periods,the understanding of microbial community composition and interaction dynamics remains incomplete.Furthermore,achieving precise control over the fermentation process is still challenging.This review examines the current state of microbial succession patterns in kimchi,paocai,suansun fermented mustard and cucumber,emphasizing critical challenges in microbial control and identifying key factors influencing community dynamics,including synergistic and competitive interactions.It also presents emerging technologies in microbial spoilage prevention,aiming to enhance microbiome-informed process control.Additionally,the review assesses metabolic pathways and sensory characteristics of fermented vegetables and highlights health risks associated with compounds like sodium nitrite,biogenic amines,and harmful microorganisms.The integration of synthetic functional microbial communities is discussed as a promising approach to improve fermentation quality.Finally,the potential for digital tools such as machine learning and industrial robotics to standardize production processes and improve quality control is addressed,highlighting future directions and practical implications for the industry.Overall,these insights support a foundation for interdisciplinary research and sustainable development in the fermented vegetable industry.
基金supported by the National Natural Science Foundation of China(Nos.61573299,61174140,61472127,and 61272395)the Social Science Foundation of Hunan Province(No.16ZDA07)+2 种基金China Postdoctoral Science Foundation(Nos.2013M540628and 2014T70767)the Natural Science Foundation of Hunan Province(Nos.14JJ3107 and 2017JJ5064)the Excellent Youth Scholars Project of Hunan Province(No.15B087)
文摘The distance dynamics model is excellent tool for uncovering the community structure of a complex network. However, one issue that must be addressed by this model is its very long computation time in large-scale networks. To identify the community structure of a large-scale network with high speed and high quality, in this paper, we propose a fast community detection algorithm, the F-Attractor, which is based on the distance dynamics model. The main contributions of the F-Attractor are as follows. First, we propose the use of two prejudgment rules from two different perspectives： node and edge. Based on these two rules, we develop a strategy of internal edge prejudgment for predicting the internal edges of the network. Internal edge prejudgment can reduce the number of edges and their neighbors that participate in the distance dynamics model. Second, we introduce a triangle distance to further enhance the speed of the interaction process in the distance dynamics model. This triangle distance uses two known distances to measure a third distance without any extra computation. We combine the above techniques to improve the distance dynamics model and then describe the community detection process of the F-Attractor. The results of an extensive series of experiments demonstrate that the F-Attractor offers high-speed community detection and high partition quality.
