dataesr/scientific-paragraphs-categorization huggingface dataset:
It is intriguing to find that both results prefer a slightly smaller value of S 8 , meaning that the HSC-Y1 and SDSS data prefer such a value or the 0. 7 0. 8 0. 9 1. 0 central values inferred from the photo-z calibration or the shear calibration might involve unknown systematic errors under the assumption of a flat ΛCDM model. This is definitely an interesting direction to further explore with upcoming large HSC datasets. S In Fig. 28  we show the results for the extended halo model, where the off-centering effect or the incompleteness effect for central galaxies is included. To model the effect we need to introduce two additional model parameters for each of the effects as can be found from Table  IV. The figure clearly shows that the cosmological constraints are robust against these variants in the theoretical templates.
Acknowledgments We would like to thank  Ryoma Murata  for his contribution during the early phase of this work. This work was supported in part by  World Premier International Research Center Initiative  ( WPI Initiative ),  MEXT, Japan , and  JSPS KAKENHI  Grant Numbers  JP15H03654 ,  JP15H05887 ,  JP15H05893 ,  JP15H05896 ,  JP15K21733 ,  JP17H01131 ,  JP17K14273 ,  JP18H04350 ,  JP18H04358 ,  JP19H00677 ,  JP19K14767 ,  JP20H00181 ,  JP20H01932 ,  JP20H04723 ,  JP20H05850 ,  JP20H05855 ,  JP20H05856 ,  JP20H05861 ,  JP21J00011 ,  JP21J10314 ,  JP21H01081 , and  JP21H05456  by  Japan Science and Technology Agency (JST) CREST   JPMHCR1414 , by  JST AIP   Acceleration Research Grant Number   JP20317829 , Japan, and by  Basic Research Grant (Super AI) of Institute for AI and Beyond of the University of Tokyo.
We thank the  LSST  Project for making their code available as free software at http://dm. lsst. org The Pan-STARRS1 Surveys (PS1) have been made possible through contributions of the  Institute for Astronomy , the  University of Hawaii , the Pan- STARRS  Project Office, the  Max-Planck Society  and its participating institutes, the  Max Planck Institute for Astronomy, Heidelberg  and the  Max Planck Institute for Extraterrestrial Physics, Garching , The  Johns Hopkins University, Durham University , the  University of Edinburgh, Queen's University Belfast , the  Harvard-Smithsonian Center for Astrophysics , the  Las Cumbres Observatory Global Telescope Network Incorporated , the  National Central University of Taiwan , the  Space Telescope Science Institute , the  National Aeronautics and Space Administration  under Grant No.
NNX08AR22G  issued through the  Planetary Science Division of the NASA Science Mission Directorate , the  National Science Foundation  under Grant No. AST-1238877 , the  University of Maryland , and  Eotvos Lorand University (ELTE)  and the  Los Alamos National Laboratory. Based in part on data collected at the  Subaru Telescope  and retrieved from the HSC data archive system, which is operated by  Subaru Telescope  and Astronomy Data Center at  National Astronomical Observatory of Japan.
For example, Thaumarchaeota have been detected in the gut of great apes and humans  18, 32  , but their presence in other animal is unknown. Finally, there is a lack of quantitative data on the abundance of archaea (and even bacteria) in the host microbiota. Overall, this lack of information has hindered the identification of factors influencing the composition, diversity and abundance of archaea in the animal gut. In this work, we analyse gut samples collected from 250 species, covering a broad spectrum of animal diversity. Using both sequencing and quantitative approaches, we investigate hostassociated archaea in eight animal classes and identify the major gut archaeal lineages, as well as the dominant methane metabolisms. Based on these data and a meta-analysis of the environmental distribution of all archaeal sequences in the Silva database we also predict several events of adaptation to the gut in the Archaea.
Results and discussion Archaea are present in the gut microbiome throughout the animal kingdom. We analysed samples from 250 species of animals (n = 341 samples) ranging from invertebrates to mammalsthe majority of which, except for birds (Aves), fish (Actinopterygii) and gastropods, came from captive specimens (Supplementary Data 1). We used three approaches to characterize the archaeal community of these samples: i) quantitative PCR (qPCR) targeting total Archaea, total Bacteria, and five archaeal lineages known to be present in the animal intestine (Methanobacteriales, Methanomassiliicoccales, Methanomicrobiales, Methanimicrococcus and Thaumarchaeota), ii) 16S rRNA gene amplicon sequencing of the Archaea only and iii) of the entire microbial community. With Archaea-specific sequencing, we detected the presence of archaea in the gut microbiome of 175 species belonging to all eight animal classes investigated, including 14 orders of mammals (Fig.
Finally, the composition of the archaeal community was largely similar across the three approaches, especially for samples with more than 10 7 archaeal 16S rRNA gene copies per gram of faeces (Supplementary Fig. 1 ). Dominance of five major unrelated lineages in the animal gut suggest independent adaptations. The broad taxonomic coverage of the animal hosts and the use of archaea-specific primers allowed us to identify archaeal ASVs belonging to 19 described families, 10 orders, 6 classes, and 3 phyla. Despite this large diversity, the majority of the archaea present in the gut are closely related to cultured ones. Indeed, 84. 9% of these ASVs (94. 5% of the reads) share more than 95% identity with cultured species in the Living Tree Project (LTP, v138) database  35  amended with characterized candidate species, and half of the reads share more than 99% identity with known species (Supplementary Table  1 ).
Consistently, the vast majority (93. 7%) of the reads are affiliated to only six genera or families (Fig. 2a ): Methanobrevibacter, Methanosphaera (Methanobacteriales), Methanomethylophilaceae (Methanomassiliicoccales), Methanocorpusculum (Methanomicrobiales), Methanimicrococcus (Methanosarcinales), Nitrososphaeraceae (Nitrososphaerales/Thaumarchaeota group 1. 1b). These lineages also constitute more than 50% of the gut archaeome in 92% of the sampled animals and can be qualified as "dominant gut archaea". Among them, Methanobrevibacter, Methanosphaera and "Ca. Methanomethylophilaceae" had already been extensively reported in the gut microbiota of ruminants, humans and termites  5, 17, 21. Methanobrevibacter members are by far the most dominant methanogens in our datasetcomposing over a third of the total number of reads, followed by "Ca.
Methanomethylophilaceae" members which accounted for 17. 5% of the total reads (Fig. 2a ). The other two most prevalent methanogen lineages, Methanocorpusculum and Methanimicrococcus, are reported less often in studies on the animal digestive tract  17, 36, 37. The five dominant methanogen lineages in the gut are rarely reported in open environments, as revealed by a meta-analysis of the archaeal 16S rRNA gene sequences present in the Silva database, covering a broad range of environments (Fig. 2a ). Moreover, these five lineages are often dominant in samples with the highest archaeal absolute abundance (Fig. 2b ). In contrast, the rarest archaeal lineages (e. g. , Methanobacterium, Methanosarcina, Methanomassiliicoccaceae) in the gut have been generally reported from non-gut environments such as sediments and wetland soils  31, 38  (Fig.
Nevertheless, the combination of the sequencing and quantitative data reveals that Nitrososphaeraceae/Thaumarchaeota are generally present in low abundance in the gut (Fig. 2c ), even in samples where they are the dominant archaea (Fig. 2b ). The three thaumarchaeal ASVs that gather the largest number of reads in our dataset (ASV4/ASV20/ASV21) are also widely distributed among animal species, ASV4 being the most widespread archaea in our samples (present in 65 animal species from 8 classes). Interestingly, these three ASVs correspond to the most prevalent and abundant archaeal phylotypes (named DSC1 and DSC2) among 146 soil samples from various biomes  44  (Fig. 2d ). Because most animals live (eat, sleep, groom) on soil, one hypothesis might be that these dominant soil archaea are ingested by chance, which could explain why they are present in a wide range of animal at a low absolute abundance (Fig.
6b ; Supplementary Fig. 13 ). At a lower host taxonomic level, the positive correlation also holds for Primates, for which we sampled species with contrasting average fibre intake (Fig. 6c ). An increased fibre consumption was previously reported to be associated with a higher expression level of methanogenesis genes in humans  59  and greater methane production in pigs  60  and ruminants  61. As the vast majority of intestinal methanogens are hydrogenotrophic, these relationships can be explained by the higher production of hydrogen from fibre/carbohydrates-rich diets (plant) than from protein/fat-rich diets (meat)  62. Fig. 3  Archaeal abundance and diversity in the animal gut (n = 150; species with ≥3000 archaeal reads). a Information on animal primary diet gathered using the Elton Trait database, the Animal Diversity Website database, or from specialists who provided faecal samples.
Methanomethylophilaceae" and Sarcina was previously reported in the human gut  82. Precursors of trimethylamine (i. e. , glycine-betaine, carnitine and choline) are present in various diets  [83] [84] [85]  and pectin is not limited to fruit but is also a constituent of the plant cell wall 86 , which therefore do not limit the presence of hydrogenotrophic methyl-reducing methanogens to frugivorous animals. In our dataset, hydrogenotrophic methyl-reducing methanogens constitute almost 40% of the overall methanogen reads (Fig. 7a ; Supplementary Table  2 ) and represent a large fraction of the methanogens in many animals (Fig. 3e ; Supplementary text). This contrasts with many non-host environments (e. g. sediments, peat bogs), where hydrogenotrophic methyl-reducing methanogens constitute a minor fraction of the overall methanogenic community  38, 87.
It also reinforces the hypothesis that the gut environment is particularly propitious for this kind of methanogenesis, which could have led to the transition from methylotrophic (methyl- dismutation) and CO 2 -reducing methanogenesis to methylreducing methanogenesis in Methanimicrococcus and Methanosphaera, respectively  36. Other methane metabolisms (based on dismutation of methyl-compounds or acetate) are almost absent from the animal gut and may occur in the few Methanosarcina members (0. 6% of the total reads) identified in our dataset (Fig. 7a ). We explored whether energetic considerations could explain why methyl-compound dismutation is not found in the gut. For this, we compared the energy available through three different metabolisms (methyl-compound dismutation, hydrogenotrophic methyl-reduction, hydrogenotrophic CO 2 -reduction), at different H 2 partial pressure (p(H 2 )) and methanol concentration (C(methanol)).
In addition, bacteria recovered from birds show little host specificity and do not display phylosymbiotic patterns with their host or correlation with diet, differently from what has been generally observed in most mammals gut microbiota  9. In our dataset, the low abundance of bacteria in the gut microbiota of bird supports the hypothesis of Hammer et al. 96  and extend the observations of Song et al. 9  on the particularity of the gut microbiota of these animals. However, we found that concentrations of faecal bacteria in other animals proposed to rely less on their gut microbiota, such as Carnivora species  96  , are as high as in other mammals (Supplementary Fig. 10b ). Comparison with a large-scale study on the animal gut archaeome. An analysis of the diversity of the gut archaeome in a wide range of animals was published late 2021  97.
This study investigated samples coming largely from wild vertebrates while our study also included invertebrates and a larger proportion of captive animals. While both studies utilized amplicon sequencing to characterize the intestinal archaeome, we also acquired quantitative data (qPCR) for all main groups of gut archaea, as well as for whole archaea and bacteria. Only 37 animal species are in common between both studies, among which 23 have archaea detected in both cases. Despite this low overlap in the taxonomic sampling, several results are congruent between the two studies. In both analyses, archaea were detected in a wide range of animals (110 species representing 69% of all species in Youngblut et al. 97  , and 175 species representing 70% of all species in our study). Collectively, the two studies detected archaea in 262 animal species, out of 372 analysed animal species (Supplementary Data 3).
Methods Sample collection and DNA extraction. A majority of animal faecal samples were donated from various zoological institutions in France (Supplementary Data 1). After consultation with the corresponding Ethics Committee in Animal Experimentation our study got direct clearance since the samples that were used for this project were collected either from species or by methods that are not in the scope of the current regulation protecting the animals used for scientific purposes (European Directive 2010/63/EU, and French Decree 2013-118). Fresh faecal samples (n = 391) from 269 species were stored at -20 °C until DNA extraction. Total DNA was extracted using a modified QIAamp PowerFecal DNA Kit (Hilden, Germany) protocol. Cells were lysed using the Fastprep (MP Biomedicals) cell homogenizer 'faecal sample' default setting in the lysis buffer provided in the PowerFecal DNA kit.
Sequencing was performed on an Illumina MiSeq platform (Biofidal, Vaulx-en-Velin, France) according to the Illumina protocols for PE 2 × 300 bp, and resulted in more than 21 million reads and more than 16. 7 million reads for the prokaryotic and archaea specific sequencing, respectively. Microbial Diversity Analyses. Reads were processed and assigned to amplicon sequence variants (ASVs) using the DADA2 software (v1. 12. 1) in R (v3. 6. 0). Briefly, reads were trimmed and quality-filtered using the standard parametersmaximum expected errors for forward and reverse reads = 2, quality score = 2, and trimming length = 273 and 170 base pairs for forward and reverse reads, respectively. Forward and reverse reads were merged with a 20 base pair overlap, ASVs were generated, and chimeras were discarded.
ASV annotation was performed using the Silva 16S rRNA database (v132). Assignment of ASVs to a main type of methane metabolism (hydrogenotrophic CO 2 -reducing, hydrogenotrophic CH 3 -reducing, acetoclastic and methylotrophic (methyl-dismutation)), was done based on their taxonomic affiliation, since all members of almost all methanogen genera/families have the same dominant type of methane metabolism (Supplementary Table  2 ). Methanosarcina is the main exception, as species from this group can have one or several types of methane metabolisms. All ASVs that were not annotated as archaea were removed from the archaea-specific primer generated sequences, and ASVs annotated as archaea or bacteria were kept from the prokaryotic universal primer generated sequences. Samples from the same species were merged by summing ASV abundances.
These approaches resulted in 1307 archaeal ASVs from the archaea specific primers, as well as 140 archaeal ASVs and 19,145 bacterial ASVs from the prokaryotic universal primers. To estimate the novelty of the archaeal ASVs (obtained with the archaea-specific primers), we compared them using BLAST to 16S rRNA genes of isolated archaea retrieved from the Silva Living Tree Project LTP database (ltp_12_2020)  102  plus additional sequences of candidate species belonging to Methanomassiliicoccales and Thaumarchaeota. For diversity analyses, rarefaction was performed to normalize sequencing depth to 3,000 reads, leading to 1,253 archaeal ASVs. Bacterial ASVs were normalized to a sequencing depth of 12,000 reads per sample. Observed richness (alpha diversity) was estimated and all beta diversity analyses were performed using the 'phyloseq' package in R (v1.
30. 0). Subsequent statistical analyses were performed using the base Rstudio 'stats' package (v3. 6. 0) as well as the R package 'vegan' (v2. 5-6). To test for significant differences using the various beta diversity metrics (Table  1 ) a permutational multivariate analysis of variation (PERMANOVA) from the R package 'vegan' (function adonis) was used. The Mantel Test from the R package 'vegan' (function mantel) and 999 permutations were used to determine the influence of the mammalian phylogeny on beta diversity metrics. The mammalian tree (n = 73) was generated using Timetree. org  103 and then converted to a distance matrix using the 'ape' (v5. 6. 1, function cophenetic. phylo) package  104  in R. A pairwise Wilcoxon rank sum test with continuity correction from the R package 'stats' (function pairwise.
wilcox. test) was used to determine differences between the absolute abundance of archaea and bacteria in animal diet types, as well as between animal classes. Linear regressions from the R package 'stats' (function lm) were used to determine the relationships between the abundance (log-transformed) of methanogens, Thaumarchaeota and bacteria, and mean retention time (MRT) and dietary fibre consumption. Significance cut-off was p < 0. 05 for all analyses. Type I errors were corrected using the Benjamini-Hochberg (BH) approach in all pairwise comparisons. Placement of ASVs within Reference 16S rRNA gene trees. All archaeal ASVs were filtered on a per sample basis, to keep only ASVs representing at least 1% of the total number of reads of the sample. Reference sequences >1200 bp with a quality >95% were obtained from the Silva SSU 138 database  35  , RDP database (11.
5)  105  , and an in-house dataset. Redundancy was removed from reference sequences with a 98% or 97% sequence identity threshold using the VSEARCH software  106. For each archaeal order, long reference sequences were combined with the ASV sequences and were aligned using the G-IN-SI algorithm in MAFFT (v7. 273)  107. Phylogenetic trees were generated using the GTR + G4 + I model in the IQTREE (1. 6. 12) software  108. For each animal, the ASVs representing more than 1% of the reads were mapped in front of the archaeal tree using ITOL  109. Reference sequences were ultimately removed from the tree to only keep the ASVs sequences. Co-occurrence of archaea and bacteria. To identify co-occurrence signal between archaea and bacteria across Mammalia, Reptilia, and Aves, we integrated the sequences from both the universal and archaea specific 16S rRNA gene amplicon sequencing.
Only bacterial reads were selected from the universal 16S rRNA gene amplicon sequencing for this analysis. We used VSEARCH (v2. 17. 1)  106  to cluster ASVs into OTUs at 97% in order to reduce the size of the dataset and to filter out ARTICLE NATURE COMMUNICATIONS | https://doi. org/10. 1038/s41467-022-31038-4 truly low abundance lineages of microbes. Then, to merge these datasets in a way that accurately represented the microbial community in terms of relative abundance between archaea and bacteria, we normalized the two datasets both in terms of sequence depth and in terms of archaea-bacterial ratios -information which was gathered through qPCR data. OTUs that were present in less than 10% of the animal classes -Mammalia, Aves, and Reptilia independently-were removed. Following this, we implemented both the SPIEC-EASI (Spiec.
Easi package v1. 1. 0,  110 ) and the SparCC algorithms  111  (part of the Spiec. Easi package (v1. 1. 0)) in Rstudio (v3. 6. 0) to determine co-occurrence trends between archaea and bacteria. Networks were calculated with 1000 iterations. The output from these analyses were filtered using a 0. 5 minimum threshold of edge stability (SPIEC-EASI) (Supplementary Data 2) and a p-value < 0. 05 (SparCC), independently. Only the co-occurrence patterns identified by both algorithms were further analysed. Investigation of archaea distribution in the gut and other environments. The 16S rRNA gene sequences from the Silva database 102 originate from thousands of studies covering all types of environments and have a robust taxonomic annotation integrating most of the phylogenetic diversity. All archaeal 16S rRNA gene sequences from Silva SSU 138 database longer than 800 bp and with more than 80% sequence quality, alignment quality and pintail quality were downloaded.
Sequences from shotgun-sequencing metagenomes were removed because their environmental origin was not clearly indicated. The annotation of each sequence was retrieved from GenBank and used to classify them as "Gut", "Environment" or "Engineered" origin. Sequences from sponge, animal environments (e. g. , nest) or polluted sites (e. g. , dump) were not included. The relative abundance of each category was mapped on a tree of archaea built with genomic sequences used in Borrel et al. 112  as well as additional DPANN sequences not present in this study. Gibbs free energies of methanogenic pathways. The following chemical reactions were considered for the calculation of Gibbs free energy changes (ΔG) associated with methanogenic pathways: Methyl-compound dismutation (CH 3 -R dismut. ): ( For each pathway, ΔG calculations were performed using the R package CHNOSZ  113  considering C(methanol) between 10 -3 and 10 -7 mol/l, p(H 2 ) between 1 and 10 -7 bar, T = 298 K, pH = 7 and p(CO 2 ) = p(CH 4 ) = 10 -1 bar.
These calculations were used to identify pathways with the highest energy yields (the lowest ΔG) per mole of methane as a function of C(methanol) and p(H2). See also  45  for another representation of the ΔG associated with these pathways as a function of p(H 2 ). Origin of the metadata. Information of animal species were collected from various literature sources and online databases. Diet information for mammals and birds were downloaded from the EltonTraits database  114  , and information for other animal diets were annotated using the Animal Diversity Web database (Museum of Zoology, University of Michigan, https://animaldiversity. org/). Information on body weight were also gathered on this website. Information about coefficients of gut differentiation, pH, diet fibre content, and intestinal tract structure and mean retention time were gather from  3, 88, [115] [116] [117] [118] [119].
112n archaeal lineages in the gut and proposed independent events of adaptation to the gut in the domain Archaea. aDistribution of archaeal 16S rRNA gene sequences in the gut and other environments based on sequences obtained from the Silva database and this study. The archaeal tree is based on Borrel et al. 112enriched with DPANN lineages. Large arrows on the tree indicate five main events of adaptation to the gut environment, small arrows indicate four other possible events of adaptation to the gut. Fully orange triangles indicate that gut adaptation likely occurred at the base of the lineage while blue triangles with an orange spot indicate that gut adaptation occurred within the lineage.
Acknowledgements C. T. is supported by a PhD fellowship from  Paris Sorbonne Université Science  and by funds from the  doctoral school Bio Sorbonne Paris Cité "BioSPC". G. B. acknowledges funding from the  French National Agency for   Research Grant Methevol  ( ANR-19-CE02-0005-01 ) and S. G. acknowledges  Archaevol  ( ANR-16-CE02-0005-01 ). This study has received funding from the  French Government 's  Investissement d'Avenir program ,  Laboratoire d'Excellence "Integrative Biology of Emerging Infectious Diseases " (grant n°ANR-10-LABX-62-IBEID). We thank the two anonymous reviewers for valuable comments. We thank the computational and storage services (TARS cluster) provided by the  IT department , in particular Sophie Creno, at  Institut Pasteur, Paris. We thank  Najwa Taib  and  Pierre Garcia  for their help with python and R scripts.