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.

Machine learning prediction model for gray-level co-occurrence matrix features of synchronous liver metastasis in colorectal cancer

BACKGROUND Synchronous liver metastasis(SLM)is a significant contributor to morbidity in colorectal cancer(CRC).There are no effective predictive device integration algorithms to predict adverse SLM events during the diagnosis of CRC.AIM To explore the risk factors for SLM in CRC and construct a visual prediction model based on gray-level co-occurrence matrix(GLCM)features collected from magnetic resonance imaging(MRI).METHODS Our study retrospectively enrolled 392 patients with CRC from Yichang Central People’s Hospital from January 2015 to May 2023.Patients were randomly divided into a training and validation group(3:7).The clinical parameters and GLCM features extracted from MRI were included as candidate variables.The prediction model was constructed using a generalized linear regression model,random forest model(RFM),and artificial neural network model.Receiver operating characteristic curves and decision curves were used to evaluate the prediction model.RESULTS Among the 392 patients,48 had SLM(12.24%).We obtained fourteen GLCM imaging data for variable screening of SLM prediction models.Inverse difference,mean sum,sum entropy,sum variance,sum of squares,energy,and difference variance were listed as candidate variables,and the prediction efficiency(area under the curve)of the subsequent RFM in the training set and internal validation set was 0.917[95%confidence interval(95%CI):0.866-0.968]and 0.09(95%CI:0.858-0.960),respectively.CONCLUSION A predictive model combining GLCM image features with machine learning can predict SLM in CRC.This model can assist clinicians in making timely and personalized clinical decisions.

冠菌素诱导橡胶树次生乳管分化的形成层细胞CUT&Tag文库构建和初步分析

CUT&Tag技术是一种研究蛋白质-DNA互作的新方法,使用超高活性的新型pG-Tn5转座酶,在抗体引导下精准靶向切割目的蛋白附近的DNA序列,从而进行cDNA建库和测序分析。此技术在人类和动物研究中应用广泛,由于植物细胞结构特殊,该技术在植物研究中应用较少。在前期研究中,我们发现组蛋白乙酰化修饰参与茉莉酸诱导橡胶树次生乳管分化的调控,但其分子机制尚未阐明。本研究利用冠菌素(COR)诱导橡胶树萌条维管形成层分化次生乳管的实验系统,通过酶解法获取高质量的形成层区细胞原生质体,使用组蛋白H3乙酰化修饰抗体对次生乳管分化过程中发生组蛋白乙酰化修饰的区域进行原位识别,采用CUT&Tag技术成功构建COR处理橡胶树树皮形成层细胞的cDNA文库。对构建cDNA文库进行质检和测序分析,发现文库质量较好,并通过差异基因的GO和KEGG富集分析,发现生长素、类黄酮代谢和蛋白质泛素化等相关基因得到富集。本研究结果为使用CUT&Tag技术构建植物组织的cDNA文库提供操作方法,为解析组蛋白乙酰化修饰调控橡胶树次生乳管分化的分子机制提供理论基础。

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.

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.

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.

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.

表达Strep-tag II的重组塞内卡病毒的构建与鉴定

为构建表达Strep-tag II的重组塞内卡病毒(SVA),本研究在感染性cDNA克隆p SVA-I212V/S460L的基础上,采用重叠延伸PCR扩增Strep-tag II并采用无缝克隆法将其插入SVA VP1基因C端,构建感染性cDNA克隆,命名为p SVA-Strep,并采用双酶切和测序鉴定正确后,将p SVA-Strep转染BHK-21细胞拯救重组病毒,收集病变细胞上清,并将含有病毒的细胞上清和野生型SVA(SVA-WT)分别感染BHK-21细胞,以未感染病毒的BHK-21细胞作为阴性对照,感染后24 h分别以鼠源SVA VP2蛋白单克隆抗体(MAb)5D10(1∶1000)、鼠源Strep-tag II MAb(1∶2000)作为一抗,以FITC标记的山羊抗小鼠Ig G(1∶2000)为二抗,采用间接免疫荧光试验(IFA)对重组病毒鉴定。IFA结果显示,以鼠源SVA VP2蛋白MAb 5D10为一抗,SVA-WT和重组病毒感染的细胞均出现绿色荧光,以Strep-tag II MAb作为一抗时,仅有重组病毒感染的细胞出现绿色荧光,经SVA-WT感染的BHK-21细胞及阴性对照细胞均无绿色荧光,表明拯救了Strep-tag II标记的重组病毒,并将其命名为r SVA-Strep。将r SVA-Strep在BHK-21细胞中连续传10代,每隔2代采用引物VP1-F/R经PCR鉴定并对第10代重组病毒的VP1基因测序以鉴定r SVA-Strep的遗传稳定性。结果显示,每隔两代的重组病毒经PCR扩增后均出现约780 bp的目的条带,第10代重组病毒扩增的VP1基因测序结果显示Strep-tag II基因仍在重组病毒中,且无基因突变,表明r SVA-Strep的遗传稳定性较强;将SVA-WT和r SVA-Strep分别以MOI 0.01感染BHK-21细胞,在感染后4 h、8 h、12 h、24 h、36 h、48 h和72 h收获病毒测定病毒滴度并绘制生长曲线,结果显示在病毒感染36 h内r SVA-Strep和SVA-WT生长动力学基本一致,表明Strep-tag II的引入在病毒感染36 h内对SVA的复制基本无影响。采用0.2%甲醛充分灭活r SVA-Strep,并利用Strep-TactinRXT Purification纯化试剂盒纯化该灭活病毒,将r SVA-Strep灭活病毒上清加至Strep-Tactin亲和层析柱,分别用不同体积洗脱缓冲液依次洗脱病毒并通过western blot鉴定病毒的纯化效果。对纯化过程中不同洗脱液洗脱病毒的western blot结果显示,不同体积的洗脱液中E1、E2和E3均出现VP0和VP2两条带,且E1和E2的条带最明显,表明洗脱缓冲液的最佳洗脱体积约为2.2 CV;以上结果表明r SVA-Strep可通过Strep-Tactin亲和层析柱一步法纯化。本研究在SVA中插入Strep-tag II标签,为SVA灭活疫苗的快速纯化提供技术手段,也为其他病原的快速纯化方法的建立提供参考。

Serum proteins differentially expressed in gestational diabetes mellitus assessed using isobaric tag for relative and absolute quantitation proteomics

BACKGROUND As a well-known fact to the public,gestational diabetes mellitus(GDM)could bring serious risks for both pregnant women and infants.During this important investigation into the linkage between GDM patients and their altered expression in the serum,proteomics techniques were deployed to detect the differentially expressed proteins(DEPs)of in the serum of GDM patients to further explore its pathogenesis,and find out possible biomarkers to forecast GDM occurrence.METHODS Subjects were divided into GDM and normal control groups according to the IADPSG diagnostic criteria.Serum samples were randomly selected from four cases in each group at 24-28 wk of gestation,and the blood samples were identified by applying iTRAQ technology combined with liquid chromatography-tandem mass spectrometry.Key proteins and signaling pathways associated with GDM were identified by bioinformatics analysis,and the expression of key proteins in serum from 12 wk to 16 wk of gestation was further verified using enzyme-linked immunosorbent assay (ELISA).RESULTS Forty-seven proteins were significantly differentially expressed by analyzing the serum samples between the GDMgravidas as well as the healthy ones. Among them, 31 proteins were found to be upregulated notably and the rest16 proteins were downregulated remarkably. Bioinformatic data report revealed abnormal expression of proteinsassociated with lipid metabolism, coagulation cascade activation, complement system and inflammatory responsein the GDM group. ELISA results showed that the contents of RBP4, as well as ANGPTL8, increased in the serumof GDM gravidas compared with the healthy ones, and this change was found to initiate from 12 wk to 16 wk ofgestation.CONCLUSION GDM symptoms may involve abnormalities in lipid metabolism, coagulation cascade activation, complementsystem and inflammatory response. RBP4 and ANGPTL8 are expected to be early predictors of GDM.

Joint Modeling of Citation Networks and User Preferences for Academic Tagging Recommender System

In the tag recommendation task on academic platforms,existing methods disregard users’customized preferences in favor of extracting tags based just on the content of the articles.Besides,it uses co-occurrence techniques and tries to combine nodes’textual content for modelling.They still do not,however,directly simulate many interactions in network learning.In order to address these issues,we present a novel system that more thoroughly integrates user preferences and citation networks into article labelling recommendations.Specifically,we first employ path similarity to quantify the degree of similarity between user labelling preferences and articles in the citation network.Then,the Commuting Matrix for massive node pair paths is used to improve computational performance.Finally,the two commonalities mentioned above are combined with the interaction paper labels based on the additivity of Poisson distribution.In addition,we also consider solving the model’s parameters by applying variational inference.Experimental results demonstrate that our suggested framework agrees and significantly outperforms the state-of-the-art baseline on two real datasets by efficiently merging the three relational data.Based on the Area Under Curve(AUC)and Mean Average Precision(MAP)analysis,the performance of the suggested task is evaluated,and it is demonstrated to have a greater solving efficiency than current techniques.

A Novel Anti-Collision Algorithm for Large Scale of UHF RFID Tags Access Systems

When the radio frequency identification(RFID)system inventories multiple tags,the recognition rate will be seriously affected due to collisions.Based on the existing dynamic frame slotted Aloha(DFSA)algorithm,a sub-frame observation and cyclic redundancy check(CRC)grouping combined dynamic framed slotted Aloha(SUBF-CGDFSA)algorithm is proposed.The algorithm combines the precise estimation method of the quantity of large-scale tags,the large-scale tags grouping mechanism based on CRC pseudo-randomcharacteristics,and the Aloha anti-collision optimization mechanism based on sub-frame observation.By grouping tags and sequentially identifying themwithin subframes,it accurately estimates the number of remaining tags and optimizes frame length accordingly to improve efficiency in large-scale RFID systems.Simulation outcomes demonstrate that this proposed algorithmcan effectively break through the system throughput bottleneck of 36.8%,which is up to 30%higher than the existing DFSA standard scheme,and has more significant advantages,which is suitable for application in largescale RFID tags scenarios.

A U-Shaped Network-Based Grid Tagging Model for Chinese Named Entity Recognition

Chinese named entity recognition(CNER)has received widespread attention as an important task of Chinese information extraction.Most previous research has focused on individually studying flat CNER,overlapped CNER,or discontinuous CNER.However,a unified CNER is often needed in real-world scenarios.Recent studies have shown that grid tagging-based methods based on character-pair relationship classification hold great potential for achieving unified NER.Nevertheless,how to enrich Chinese character-pair grid representations and capture deeper dependencies between character pairs to improve entity recognition performance remains an unresolved challenge.In this study,we enhance the character-pair grid representation by incorporating both local and global information.Significantly,we introduce a new approach by considering the character-pair grid representation matrix as a specialized image,converting the classification of character-pair relationships into a pixel-level semantic segmentation task.We devise a U-shaped network to extract multi-scale and deeper semantic information from the grid image,allowing for a more comprehensive understanding of associative features between character pairs.This approach leads to improved accuracy in predicting their relationships,ultimately enhancing entity recognition performance.We conducted experiments on two public CNER datasets in the biomedical domain,namely CMeEE-V2 and Diakg.The results demonstrate the effectiveness of our approach,which achieves F1-score improvements of 7.29 percentage points and 1.64 percentage points compared to the current state-of-the-art(SOTA)models,respectively.

Differential Expression Analysis of Proteins Regulated by Rare Earth Cerium in Soybean Leaves at Seedling Stage

This study was to explore the functional mechanism of rare earth regulating soybean leaves and the characteristics and functions of differentially expressed proteins under the regulation of rare earth. In this study, Dongnong 42 was used as material, and 30 mg·L^(-1) CeCl_(3) solution was sprayed on soybean leaves at the seedling stage. Tandem mass tag(TMT) quantitative proteomics technique and bioinformatics analysis were used to identify soybean leaf proteins. A total of 8 510 proteins were identified, and 127 differentially expressed proteins(DEPs) in response to rare earth cerium regulation were identified, among which 64 were upregulated and 63 were down-regulated. The gene ontology(GO) annotation indicated that DEPs were mainly involved in metabolic process, cellular process, response to stimulus, biological regulation, and response to a stimulus;DEPs in cell module categories were mainly involved in cells, cell part, organelle, membrane, membrane part, organelle par, and protein-containing complex;DEPs in molecular functional categories were mainly involved in catalytic activity, binding and antioxidant activity. Kyoto encyclopedia of genes and genomes(KEGG) pathway significantly enriched starch and sucrose metabolism, glycolysis/gluconeogenesis, galactose metabolism, pentose phosphate pathway, and MAPK signaling pathway-plant. These DEPs were mainly involved in photosynthesis, glucose metabolism and stress response. Forty-six differential protein interaction networks were identified by protein interaction network analysis. This experiment provided a reference for studies of the mechanism of rare earth cerium regulating soybean leaf function from the proteomic perspective.

34.TAG·西海美术馆:面朝大海的艺术长廊

设计:让·努维尔事务所地点:山东青岛西海岸新区时间:2012—2021年规模:17000 m^(2)TAG·西海美术馆位于一座半岛的海边,远离市区^([1,2])。从规划意图看,整个项目由美术馆和艺术工坊两部分组成,但由于工坊部分处于停工状态,艺术社区氛围尚未形成,作为主体建筑的美术馆便成了荒野中的孤独存在,公众可达性较差(图1)。尽管如此,从设计研究角度,这座美术馆仍有许多精彩看点值得关注。

TAG HEUER 竞速激情

继去年以卡莱拉系列Skipper计时码表的精钢版本标志着品牌重返帆船运动与航海领域之后,TAG Heuer泰格豪雅如今欣然发布一款以18K 5N玫瑰金打造的全新版本。金质的Skipper计时码表与精钢版本类似,同样基于2023年前期为纪念卡莱拉系列计时码表60周年推出且热销的“玻璃盒”(glassbox)设计,并搭载品牌自制机心Heuer 02(型号:TH20-06),这是一款为帆船赛计时而特别调整的Heuer 02机心。

TAG HEUER 传奇之作 全面提升

传奇的卡莱拉泛美公路赛以其在墨西哥的严苛挑战而闻名,杰克·豪雅(Jack Heuer)以此为灵感,为TAG HEUER泰格豪雅卡莱拉系列腕表赋予了这一独特且极具影响力的名字。该系列凭借持续挑战边界的精准度,迅速赢得了市场的青睐并占据表迷的腕间,时至今日依旧风靡全球。在对系列进行了无数次的演绎后,日前泰格豪雅再度带来多款全新作品,将竞速传承、前沿材质及创新设计巧妙融合,一方面,是对2016年焕新的备受赞誉的卡莱拉系列运动风格计时码表的大胆演绎,另一方面,标志着该系列的又一次升级演变。

一种含SNAP-tag TM底物的硫醇荧光探针

合成了一种同时含α,β-不饱和酮活性位点和SNAP-tag TM底物的香豆素母体探针香豆素-烯酮-苯甲氨基鸟嘌呤(CAKBG).通过测试探针CAKBG的紫外-可见吸收光谱和荧光发射光谱,发现探针CAKBG在极性溶剂(例如,甘油、乙二醇、水、DMSO)中有更长的吸收波长,且探针CAKBG对溶液的pH变化不敏感.此外,光谱实验发现生物硫醇Cys和Hcy均能引起探针CAKBG的最大吸收波长发生蓝移,且当激发波长为405 nm时,Cys和Hcy均能导致探针CAKBG在487 nm处的荧光发射峰明显增强.细胞成像结果表明,含有SNAP-tag TM底物的硫醇荧光探针CAKBG在活细胞中可以对Cys、Hcy进行荧光成像.综上,SNAP-Tag TM蛋白标签技术有望今后在硫醇荧光探针的细胞成像中得到推广应用.

基于co-occurrence相似度的聚类集成方法

首先提出了一种基于属性值的co-occurrence相似度概念,通过对其进一步的研究,提出了3个等价性表述;然后对属性值之间的co-occurrence相似度进行引申,给出了数据对象之间co-occurrence相似度的定义,并将其成功应用到聚类集成方法中。利用co-occurrence相似度在计算某个初始聚类结果中数据对象之间的相似度时,充分考虑了其他初始聚类结果和该初始聚类结果之间的相互影响和联系。实验表明,基于co-occurrence相似度的聚类集成(CSCE)方法能有效识别数据之间的细微结构,有助于提高聚类集成的效果。

基于加权co-occurrence矩阵的聚类集成算法

聚类集成是数据挖掘研究的一个热点。它是利用同一数据集的多个聚类划分集成在一起,以提高聚类分析的性能。当前相关研究大多没有考虑进行集成的聚类成员的质量,因此较差的成员会对集成结果产生不良影响。文中提出了一种基于加权co-occurrence矩阵的聚类集成算法(WCSCE)。该方法首先计算出聚类成员基于属性值的co-occurrence矩阵,然后对聚类成员的质量进行简单评价并赋予权重,生成加权co-occurrence矩阵,进而产生集成结果。最后通过实验验证了该算法的有效性,并提高了聚类质量。

Good and bad dispositions between archaea and bacteria in the human gut:New insights from metagenomic survey and co-occurrence analysis

Archaea are an understudied component of the human microbiome.In this study,the gut archaeome and bacteriome of 60 healthy adults from different region were analyzed by whole-genome shotgun sequencing.Archaea were ubiquitously found in a wide range of abundances,reaching up to 7.2%.The dominant archaeal phylum was Methanobacteriota,specifically the family Methanobacteriaceae,encompassing more than 50%of Archaea in 50 samples.The previously underestimated Thermoplasmatota,mostly composed of Methanomassiliicoccaceae,dominated in 10 subjects(>50%)and was present in all others except one.Halobacteriota,the sole other archaeal phylum,occurred in negligible concentration,except for two samples(4.6-4.8%).This finding confirmed that the human gut archaeome is primarily composed of methanogenic organisms and among the known methanogenic pathway:i)hydrogenotrophic reduction of CO_(2) is the predominant,being the genus Methanobrevibacter and the species Methanobrevibacter smithii the most abundant in the majority of the samples;ii)the second pathway,that involved Methanomassiliicoccales,was the hydrogenotrophic reduction of methyl-compounds;iii)dismutation of acetate or methyl-compounds seemed to be absent.Co-occurrence analysis allowed to unravel correlations between Archaea and Bacteria that shapes the overall structure of the microbial community,allowing to depict a clearer picture of the human gut archaeome.