Effect of land use on soil nematode community composition and co-occurrence network relationship

Land use influences soil biota community composition and diversity,and then belowground ecosystem processes and functions.To characterize the effect of land use on soil biota,soil nematode communities in crop land,forest land and fallow land were investigated in six regions of northern China.Generic richness,diversity,abundance and biomass of soil nematodes was the lowest in crop land.The richness and diversity of soil nematodes were 28.8and 15.1%higher in fallow land than in crop land,respectively.No significant differences in soil nematode indices were found between forest land and fallow land,but their network keystone genera composition was different.Among the keystone genera,50%of forest land genera were omnivores-predators and 36%of fallow land genera were bacterivores.The proportion of fungivores in forest land was 20.8%lower than in fallow land.The network complexity and the stability were lower in crop land than forest land and fallow land.Soil pH,NH_(4)^(+)-N and NO_(3)^(–)-N were the major factors influencing the soil nematode community in crop land while soil organic carbon and moisture were the major factors in forest land.Soil nematode communities in crop land influenced by artificial management practices were more dependent on the soil environment than communities in forest land and fallow land.Land use induced soil environment variation and altered network relationships by influencing trophic group proportions among keystone nematode genera.

Inversion tillage with straw incorporation affects the patterns of soil microbial co-occurrence and multi-nutrient cycling in a Hapli-Udic Cambisol

Inversion tillage with straw amendment is widely applied in northeastern China, and it can substantially increase the storage of carbon and improve multiple subsoil functions. Soil microorganisms are believed to be the key to this process,but research into their role in subsoil amelioration is limited. Therefore, a field experiment was conducted in 2018 in a region in northeastern China with Hapli-Udic Cambisol using four treatments: conventional tillage(CT, tillage to a depth of 15 cm with no straw incorporation), straw incorporation with conventional tillage(SCT, tillage to a depth of 15 cm),inversion tillage(IT, tillage to a depth of 35 cm) and straw incorporation with inversion tillage(SIT, tillage to a depth of 35 cm). The soils were managed by inversion to a depth of 15 or 35 cm every year after harvest. The results indicated that SIT improved soil multi-nutrient cycling variables and increased the availability of key nutrients such as soil organic carbon, total nitrogen, available nitrogen, available phosphorus and available potassium in both the topsoil and subsoil.In contrast to CT and SCT, SIT created a looser microbial network structure but with highly centralized clusters by reducing the topological properties of average connectivity and node number, and by increasing the average path length and the modularity. A Random Forest analysis found that the average path length and the clustering coefficient were the main determinants of soil multi-nutrient cycling. These findings suggested that SIT can be an effective option for improving soil multi-nutrient cycling and the structure of microbial networks, and they provide crucial information about the microbial strategies that drive the decomposition of straw in Hapli-Udic Cambisol.

Environment drives the co-occurrence of bacteria and microeukaryotes in a typical subtropical bay

The co-occurrence of bacteria and microeukaryote species is a ubiquitous ecological phenomenon,but there is limited cross-domain research in aquatic environments.We conducted a network statistical analysis and visualization of microbial cross-domain co-occurrence patterns based on DNA sampling of a typical subtropical bay during four seasons,using high-throughput sequencing of both 18S rRNA and 16S rRNA genes.First,we found obvious relationships between network stability and network complexity indices.For example,increased cooperation and modularity were found to weaken the stability of cross-domain networks.Secondly,we found that bacterial operational taxonomic units(OTUs)were the most important contributors to network complexity and stability as they occupied more nodes,constituted more keystone OTUs,built more connections,more importantly,ignoring bacteria led to greater variation in network robustness.Gammaproteobacteria,Alphaproteobacteria,Bacteroidetes,and Actinobacteria were the most ecologically important groups.Finally,we found that the environmental drivers most associated with cross-domain networks varied across seasons(in detail,the network in January was primarily constrained by temperature and salinity,the network in April was primarily constrained by depth and temperature,the network in July was mainly affected by depth,temperature,and salinity,depth was the most important factor affecting the network in October)and that environmental influence was stronger on bacteria than on microeukaryotes.

Insights into microbiota community dynamics and flavor development mechanism during golden pomfret(Trachinotus ovatus)fermentation based on single-molecule real-time sequencing and molecular networking analysis

Popular fermented golden pomfret(Trachinotus ovatus)is prepared via spontaneous fermentation;however,the mechanisms underlying the regulation of its flavor development remain unclear.This study shows the roles of the complex microbiota and the dynamic changes in microbial community and flavor compounds during fish fermentation.Single-molecule real-time sequencing and molecular networking analysis revealed the correlations among different microbial genera and the relationships between microbial taxa and volatile compounds.Mechanisms underlying flavor development were also elucidated via KEGG based functional annotations.Clostridium,Shewanella,and Staphylococcus were the dominant microbial genera.Forty-nine volatile compounds were detected in the fermented fish samples,with thirteen identified as characteristic volatile compounds(ROAV>1).Volatile profiles resulted from the interactions among the microorganisms and derived enzymes,with the main metabolic pathways being amino acid biosynthesis/metabolism,carbon metabolism,and glycolysis/gluconeogenesis.This study demonstrated the approaches for distinguishing key microbiota associated with volatile compounds and monitoring the industrial production of high-quality fermented fish products.

Temporal characteristics of algae-denitrifying bacteria co-occurrence patterns and denitrifier assembly in epiphytic biofilms on submerged macrophytes in Caohai Lake,SW China

Denitrifying bacteria in epiphytic biofilms play a crucial role in nitrogen cycle in aquatic habitats.However,little is known about the connection between algae and denitrifying bacteria and their assembly processes in epiphytic biofilms.Epiphytic biofilms were collected from submerged macrophytes(Patamogeton lucens and Najas marina L.)in the Caohai Lake,Guizhou,SW China,from July to November 2020 to:(1)investigate the impact of abiotic and biotic variables on denitrifying bacterial communities;(2)investigate the temporal variation of the algae-denitrifying bacteria co-occurrence networks;and(3)determine the contribution of deterministic and stochastic processes to the formation of denitrifying bacterial communities.Abiotic and biotic factors influenced the variation in the denitrifying bacterial community,as shown in the Mantel test.The co-occurrence network analysis unveiled intricate interactions among algae to denitrifying bacteria.Denitrifying bacterial community co-occurrence network complexity(larger average degrees representing stronger network complexity)increased continuously from July to September and decreased in October before increasing in November.The co-occurrence network complexity of the algae and nirS-encoding denitrifying bacteria tended to increase from July to November.The co-occurrence network complexity of the algal and denitrifying bacterial communities was modified by ammonia nitrogen(NH_(4)^(+)-N)and total phosphorus(TP),pH,and water temperature(WT),according to the ordinary least-squares(OLS)model.The modified stochasticity ratio(MST)results reveal that deterministic selection dominated the assembly of denitrifying bacterial communities.The influence of environmental variables to denitrifying bacterial communities,as well as characteristics of algal-bacterial co-occurrence networks and the assembly process of denitrifying bacterial communities,were discovered in epiphytic biofilms in this study.The findings could aid in the appropriate understanding and use of epiphytic biofilms denitrification function,as well as the enhancement of water quality.

Community composition,co-occurrence,and environmental drivers of bacterioplankton community in surface and 50-m water layers in the subarctic North Pacific

The Western Subarctic Gyre(WSG)is one of the two gyre-systems in the subarctic North Pacific known for high nutrient and low-chlorophyll waters.However,the bacterioplankton in marine water of this area,either in terms of the taxonomic composition or functional structure,remains relatively unexplored.A total of 22 sampling sites from two water layers(surface water,SW and 50-m layer water,FW)were collected in this area.The physiochemical parameters of waters,Synechococcus,and bacterial density,as well as the bacterioplankton community composition and distribution pattern,were analyzed.The nutrient concentrations of DIN,DIP,and DSi,Chl-a concentration,and the average abundance of heterobacteria in FW were higher than those in SW.However,temperature and the average abundance of Synechococcus and pico-eukaryotes were higher in SW.A total of 3269 OTUs were assigned,and 2123OTUs were commonly shared;moreover,similar alpha diversity patterns were observed in both SW and FW.The bacterioplankton community showed significantly obvious correlation with salinity,DIP,DIN,and Chl a in both SW and FW.Proteobacteria,Cyanobacteria,Bacteroidota,Actinobacteriota,and Firmicutes were the main phyla while Synechococcus_CC9902,Psychrobacter,and Sulfitobacter were the dominant genera in each sampling site.Most correlations that happened between the OTUs in the cooccurrence network were positive and inter-module.Higher edges and graph density were found in SW,indicating that more correlations occurred,and the community was more complex in SW.This study provided novel knowledge on the bacterioplankton community structure and the correlation characteristics in WSG.

A Metric Approach to Hot Topics in Biomedicine via Keyword Co-occurrence

Purpose:To reveal the research hotpots and relationship among three research hot topics in b iomedicine,namely CRISPR,iPS(induced Pluripotent Stem)cell and Synthetic biology.Design/methodology/approach:We set up their keyword co-occurrence networks with using three indicators and information visualization for metric analysis.Findings:The results reveal the main research hotspots in the three topics are different,but the overlapping keywords in the three topics indicate that they are mutually integrated and interacted each other.Research limitations:All analyses use keywords,without any other forms.Practical implications:We try to find the information distribution and structure of these three hot topics for revealing their research status and interactions,and for promoting biomedical developments.Originality/value:We chose the core keywords in three research hot topics in biomedicine by using h-index.

Keyword Co-occurrence Analysis of Research on Innovation Ecosystem Studies

The aim of the paper is to identify and explore leading thematic areas within the research field related to innovation ecosystem.Based on data from the Web of Science database,the keywords frequency and its co-occurrence frequency pair were analyzed,and the theory of mapping knowledge domains was used to visualize the keywords co-occurrence network in innovation ecosystem to make further research of the heated issues.The findings indicate that the research scope involved in innovation ecosystem research is broad,and research content focus on micro and macro levels.According to the results of keywords co-occurrence analysis of different stages,innovation,network,knowledge,strategy,open innovation,value creation are the most important issues to innovation ecosystem research,given their position and role in the research network.The paper generates the added value mainly from the point of view of theory development.

Assembly and co-occurrence patterns of rare and abundant bacterial sub-communities in rice rhizosphere soil under short-term nitrogen deep placement

Nitrogen(N)deep placement has been found to reduce N leaching and increase N use efficiency in paddy fields.However,relatively little is known how bacterial consortia,especially abundant and rare taxa,respond to N deep placement,which is critical for understanding the biodiversity and function of agricultural ecosystem.In this study,lllumina sequencing and ecological models were conducted to examine the diversity patterns and underlying assembly mechanisms of abundant and rare taxa in rice rhizosphere soil under different N fertilization regimes at four rice growth stages in paddy fields.The results showed that abundant and rare bacteria had distinct distribution patterns in rhizosphere samples.Abundant bacteria showed ubiquitous distribution;while rare taxa exhibited uneven distribution across all samples.Stochastic processes dominated community assembly of both abundant and rare bacteria,with dispersal limitation playing a more vital role in abundant bacteria,and undominated processes playing a more important role in rare bacteria.The N deep placement was associated with a greater influence of dispersal limitation than the broadcast N fertilizer(BN)and no N fertilizer(NN)treatments in abundant and rare taxa of rhizosphere soil;while greater contributions from homogenizing dispersal were observed for BN and NN in rare taxa.Network analysis indicated that abundant taxa with closer relationships were usually more likely to occupy the central position of the network than rare taxa.Nevertheless,most of the keystone species were rare taxa and might have played essential roles in maintaining the network stability.Overall,these findings highlighted that the ecological mechanisms and co-occurrence patterns of abundant and rare bacteria in rhizosphere soil under N deep placement.

Application of the Keyword Recognition in the Network Monitoring

In this paper,the specific application of key words spotting used in the network monitoring is studied,and the keywords spotting is emphasized.The whole monitoring system is divided into two modules:network monitoring and keywords spotting.In the part of network monitoring,this paper adopts a method which is based on ARP spoofing technology to monitor the users’ data,and to obtain the original audio streams.In the part of keywords spotting,the extraction methods of PLP(one of the main characteristic parameters)is studied,and improved feature parameters-PMCC are put forward.Meanwhile,in order to accurately detect syllable,the paper compares the double-threshold method with variance of frequency band method,and use the latter to carry out endpoint detection.Finally,keywords recognition module is built by HMM,and identification results are contrasted under Matlab environment.From the experiment results,a better solution for the application of key words recognition technology in network monitoring is found.

集合空间关键字内聚组查询方法

给定一个道路网络和社交网络,集合空间关键字查询的目的是找到一组兴趣点,该组兴趣点的文本信息包含所有查询关键字,与查询的位置较近且彼此之间的距离较小。内聚组查询的目的是找到在地理位置和社交关系上紧密联系的一组用户;而集合空间关键字内聚组查询的目的是找到满足查询要求的一对最佳匹配的兴趣点集合和用户集合。针对这一问题,提出一种新的集合空间关键字内聚组查询处理模式。首先通过快速贪心查询过程获得候选兴趣点集合,然后使用core-tree结构存储(k,c)-core核心分解的结果,从而提高内聚组查询效率,并且保证查询结果能够同时满足用户之间的社会关系约束和兴趣点之间的空间位置约束。通过在真实数据集上开展实验,结果表明提出的方法比枚举方法的查询效率快1~2个数量级,并且具有较高查询准确性。

中国农村人居环境政策的扩散特征——基于政策文本的量化研究

扎实推进农村人居环境治理政策是实现乡村振兴战略的重要内容,也是建设美丽中国的关键举措。文章基于政策扩散理论,采用社交网络分析法和关键词时序分析法并借助Gephi软件,对2001年1月至2022年7月中国农村人居环境政策文本进行量化分析,旨在揭示其扩散过程和特征。结果发现:在时间维度上,中国农村人居环境政策的扩散特征呈现S型形态;在强度维度上,方案类和意见类政策扩散强度较高,且具有显著的时序集聚性特征;在广度和速度维度上,方案类政策相较于意见类政策,覆盖范围更广、速度更快,并呈急剧下滑趋势。在方向维度上,通过“村庄规划”和“危房改造”政策工具的关键词时序分析,发现两者均呈现出同级间平行扩散和地方向中央由上至下的扩散方向。本研究揭示了中国农村人居环境政策扩散的复杂性和多层次性,有助于更好地理解政策传播的规律,为未来农村人居环境政策的制定和实施提供有益的启示。

基于异构图和关键词的抽取式文本摘要模型

抽取式文本摘要使用一定的策略从冗长的文本中选择一些句子组成摘要,其关键在于要尽可能多地利用文本的语义信息和结构信息。为了更好地挖掘这些信息,进而利用它们指导摘要的抽取,提出了一种基于异构图和关键词的抽取式文本摘要模型(HGKSum)。该模型首先将文本建模为由句子节点和词语节点构成的异构图,在异构图上使用图注意力网络学习节点的特征,之后将关键词抽取任务作为文本摘要任务的辅助任务,使用多任务学习的方式进行训练,得到候选摘要,最后对候选摘要进行精炼以降低冗余度,得到最终摘要。在基准数据集上的对比实验表明,该模型性能优于基准模型,此外,消融实验也证明了引入异构节点和关键词的必要性。

基于时间的关键字路网路径规划

为了便于道路网距离的计算、对象文本信息和时间信息的组织和快速获取,改进G-Tree,在索引结构上增加时间戳和倒排序列相关信息,称包含时间戳和倒排序列相关信息的G-Tree为IGT-Tree.并基于构建的IGT-Tree设计了两种推荐算法:一种是基于时间的目标点查询算法,另一种是在目标点查询算法基础上的路径推荐算法.最后,在基于时间的关键字路网通过实验对比G-Tree的算法验证了所提算法的高效性.

Soil bacterial and fungal communities resilience to long-term nitrogen addition in subtropical forests in China

Atmospheric nitrogen(N)deposition is predicted to increase,especially in the subtropics.However,the responses of soil microorganisms to long-term N addition at the molecular level in N-rich subtropical forests have not been clarified.A long-term nutrient addition experiment was conducted in a subtropical evergreen old-growth forest in China.The four treatments were:control,low N(50 kg N ha^(-1)a^(-1)),high N(100 kg N ha^(-1)a^(-1)),and combined N and phosphorus(P)(100 kg N ha^(-1)a^(-1)+50 kg P ha^(-1)a^(-1)).Metagenomic sequencing characterized diversity and composition of soil microbial communities and used to construct bacterial/fungal co-occurrence networks.Nutrient-treated soils were more acidic and had higher levels of dissolved organic carbon than controls.There were no significant differences in microbial diversity and community composition across treatments.The addition of nutrients increased the abundance of copiotrophic bacteria and potentially beneficial microorganisms(e.g.,Gemmatimonadetes,Chaetomium,and Aureobasidium).Low N addition increased microbiome network connectivity.Three rare fungi were identified as module hubs under nutrient addition,indicating that low abundance fungi were more sensitive to increased nutrients.The results indicate that the overall composition of microbial communities was stable but not static to long-term N addition.Our findings provide new insights that can aid predictions of the response of soil microbial communities to long-term N addition.

Effect of soil archaea on N_(2)O emission in alpine permafrost

Soil microbial communities are pivotal in permafrost biogeochemical cycles,yet the variations of abundant and rare microbial taxa and their impacts on greenhouse gas emissions in different seasons,remain elusive,especially in the case of soil archaea.Here,we conducted a study on soil abundant and rare archaeal taxa during the growing and non-growing seasons in the active layer of alpine permafrost in the Qinghai-Tibetan Plateau.The results suggested that,for the archaeal communities in the sub-layer,abundant taxa exhibited higher diversity,while rare taxa maintained a more stable composition from the growing to non-growing season.Water soluble organic carbon and soil porosity were the most significant environmental variables affecting the compositions of abundant and rare taxa,respectively.Stochastic and deterministic processes dominated the assemblies of rare and abundant taxa,respectively.The archaeal ecological network influenced N_(2)O flux through different modules.Rare taxa performed an essential role in stabilizing the network and exerting important effects on N_(2)O flux.Our study provides a pioneering and comprehensive investigation aimed at unravelling the mechanisms by which archaea or other microorganisms influence greenhouse gas emissions in the alpine permafrost.

Determining future high speed rail review topics through bibliometric analysis

As High Speed Rail(HSR)has proliferated globally,so has a related research field dedicated to exploring and addressing its unique issues.Yet,studies to understand and classify the HSR research domain are limited.This paper addresses the gap,using bibliometric analysis to identify future research areas and 20 candidate topics for literature review based on keyword analysis through VOSviewer.Article and review papers related to HSR published in the last 20 years(2003-2022)were retrieved from Scopus,and then analyzed to determine the split in knowledge between languages,the collaboration between countries and institutions,highly productive and cited journals,and research topics which have and have not been reviewed.Approximately 30%of the search results were published exclusively in Chinese,highlighting the importance of extending the evaluation to cover both languages.This is a novel aspect of the work,which has enabled the recognition of potential knowledge gaps.It is recommended that future reviews incorporate works in both languages,possibly through international collaboration.Institutions in China and other countries that are strong collaborators have been identified,as well as relevant,highly cited journals.

基于SRAM缓存和存内计算的低功耗关键词唤醒系统

为了解决关键词唤醒算法部署在边缘计算硬件会带来较高功耗、给电池驱动的设备带来续航挑战的问题,提出了一种基于存内计算技术和软硬件协同优化的低功耗关键词唤醒系统。在算法层面,基于标准MFCC算法拓扑结构提出了一种三值量化MFCC-CNN联合算法,将MFCC中的全部通用矩阵乘映射到神经网络加速器当中。在电路层面,提出了一种基于SRAM的存内计算核心,用于解决传统冯·诺依曼架构加速器存在的功耗墙和存储墙问题。同时通过复用存内计算核心的SRAM存储功能提出了一种基于查找表实现的缓存电路,用于替代寄存器延迟链电路。SRAM存内计算核心和SRAM缓存电路均采用定制单元实现。在系统层面,基于以上2种定制电路设计了一种低功耗关键词唤醒系统。该系统采用ASIC与定制化电路设计流程设计,并使用28 nm CMOS工艺库对该设计进行了ASIC综合,在250 kHz下,关键词唤醒系统运行10分类任务的延迟是64 ms,整体功耗为645.28μW,其中MFCC流水线的动态功耗占总动态功耗的5.9%,总功耗仅占系统功耗的1.3%。

基于关键词加权的混合特征文本快速分类仿真

电子文本形式的网络信息不仅数量多,且混合特征具有较高相似性,很难达到特征的平均分布。特征项在类别间的不均性导致文本权重计算易出现偏差,影响类别特征词的提取,导致文本分类难度较大。为此,提出一种基于关键词加权的混合特征文本快速分类方法。采用词频逆文本频率指数信息检索方法对文本加权,计算不同权重下文本关键词在中心集合中出现的频率。根据频率阈值提取关键特征,确定文本集合中类中心点。计算与类中心相关性最高的文本数据,提取关联度特征。建立神经网络分类模型,预先设定一组包含详细特征的文本集,作为初始值输入到神经网络中,每个层次根据目标特征逐一比对实现有效分类。实验证明,所研究方法的查全率更高,文本混合特征提取的召回率高于40%,说明研究方法应用性能更优,对不同种类的文本集均能完成精准分类。