dataesr/scientific-paragraphs-categorization huggingface dataset:
The experiments were performed for July 2016 in a domain covering a large part of North America at a spatial resolution of 27 km. Most inversion experiments assimilated observations using a 1-week assimilation window. Under ideal conditions where the true flux error covariances are known, TRACE was able to reconstruct the true fluxes to a large degree; the posterior fluxes reduced the errors in monthly domain-integrated NEE by about 97% relative to the prior flux errors, and with spatial correlations of r = 0. 87 and above between the posterior and true fluxes. In the second set of experiments, we considered a more realistic case where the true flux errors and their statistics are unknown. The true fluxes in these experiments were derived from different datasets than the prior fluxes, and three different prior flux parameter error correlations were tested.
Data Availability Statement All data used in this study are publicly available. The prior fluxes from CT2019B can be downloaded from the CarbonTracker website: https://gml. noaa. gov/ccgg/carbontracker/download. php. The WRF-Chem model is freely distributed to the scientific community by NCAR at https://www2. mmm. ucar. edu/wrf/users/download/get_source. html. The code for the EnKF algorithm is provided by the Penn State Center for Advanced Data Assimilation and Predictability Techniques (ADAPT) at http://adapt. psu. edu/index. php?loc=outreach under the Download Links section. Data and scripts supporting this study are available through Zenodo at https://doi. org/10. 5281/zenodo. 6583536.
3 in dark compared with subsequent exposure to 2 h light (Supplementary Table  21 ), both in sigRep and WT. Nevertheless, the difference in averaged 18 S Cq between sigRep and WT from the same conditions was always ≤ 0. 9, which indicated sufficient consistency in 18 S expression regardless of the cell line and photooxidative stress. These results indicated suitability of 18 S to be used as a reference gene for qRT-PCR experiments. Most primers for transcript analyses were designed using Primer3Plus (http://primer3plus. com). Wherever applicable, the same primers were used as in the previous gene expression studies. All primers and their references are listed in Supplementary Table  20. RNA-sequencing was performed on total RNA samples isolated from biological triplicates of sigRep and gunSOS1 after the shift from dark to 20 µmol photons m -2 s -1 light for 2 h.
Library preparation, sequencing, and analysis services were commercially provided by Novogene Europe (Novogene (UK) Company Ltd. , Cambridge, UK) and performed using an Illumina NovaSeq 6000 platform operated in 150 bp pair-end sequencing mode, with a sequencing depth of 20 million reads per sample. Six gigabases of sequencing data per library was filtered for high-quality reads, which were mapped to the C. reinhardtii v5. 5 (Department of Energy JGI). Estimated expression was obtained in fragments per kilobase of transcript sequence per millions base pairs sequenced (FPKM). Biological replicates were averaged to obtain sample expression estimate. Differential expression analysis between two mutants (three biological replicates per mutant) was performed using DESeq2. v1. 20. 0, which provides statistical routines for determining differential expression using a model based on the negative binomial distribution.
Statistics and reproducibility. Statistical analyses were conducted using the GraphPad Prism 9 for Windows, version 9. 5. 0 (GraphPad Software, San Diego, California USA, www. graphpad. com). All the details concerning particular analysis are included in the main text, in the Figure  legend , or in the Supplementary Table, wherever applicable. Open Access This article is licensed under a Creative Commons Attribution 4. 0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material.
Acknowledgements This work was supported by the  Deutsche Forschungsgemeinschaft  ( DFG-GR 936/20-2  to B. G. and P. B. ), by the  Agence Nationale de la Recherche  (ChloroPaths:  ANR-14-CE05-0041-01  to X. J. ), and by the  Max Planck Society  (R. F. and J. E. L. ). We acknowledge support by the  Open Access Publication Fund of Humboldt-Universität zu Berlin.
Data availability All data relevant for interpretation of this study are presented in the article and supplementary material, including uncropped and unedited blot images for Fig. 2d  (Supplementary Fig. 12 ). The numerical source data for all the graphs presented in the main Figures are provided in Supplementary Data 4. The raw RNA-seq data were deposited in the National Center for Biotechnology Information Sequence Read Archive, accession number PRJNA954977. Any further information is available from the corresponding author upon reasonable request.
05. Recurrence was defined as any lesion identified on biopsy and histologically diagnosed as recurrent invasive cervical cancer. DFS was defined as the time (in months) from the date of the initial histological diagnosis to the date of the first histological diagnosis of recurrence or death. Patients who did not have recurrence or death were censored at the date of last follow-up. OS was defined as the time (in months) from the date of the initial histological diagnosis to the date of death (all causes). Patients who were still alive at the cut-off date were censored. Survival data were available for all patients in 2018, allowing for at least 3 years of follow-up for all patients. Survival curves were compared using the log rank test. All statistical analyses were performed using RStudio Team (2020) software.
| Patients features Among 490 patients with early-stage cervical cancer, 302 were excluded because the FIGO stage was greater than IB2, 6 were excluded because they had pTNM stage III, 8 were excluded due to missing data about the treatments received, 6 because they did not undergo surgery, 27 because they received adjuvant pelvic radiation after primary surgery, 3 because they received concomitant radiochemotherapy, and 12 because the radiation dose received was either <50 Gy, >66 Gy or not documented (Figure  1 ). Finally, 126 patients were included in the present analysis: 80 patients in the surgery group, and 46 in the radiation group. Average age at diagnosis was 46. 9 years; 65 patients had stage IA tumors, and 61 had stage ≥IB1. The predominant histological type was squamous cell carcinoma (84.
, 2020). Additionally, we sequenced human fecal samples from two African populations  (Mossoun et al. , 2017)  from rural villages of the Taï region in Côte d'Ivoire (HDI2021 = 0. 550; (UNDP (United Nations Development Programme), 2022)) and the Bandundu region near Salonga National Park, Democratic Republic of the Congo (HDI2021 = 0. 479), along with samples from Germany (HDI2021 = 0. 942), and included a published dataset from Denmark (HDI2021 = 0. 948;  (Hansen et al. , 2018) ) to incorporate varying degrees of HDI. Using this extensive data resource, we created a comprehensive catalog of high-quality prokaryotic genomes assembled from metagenomic data, which we annotated on a taxonomic and functional level. We subsequently explored patterns of diversity and host-specificity for both taxonomic groups and functions, which reveals intriguing patterns associated with human gut microbial communities, convergent functional adaptations across lineages, and the potential mechanisms driving these patterns.
An expanded catalog of microbial genomes from the hominid gut Using 224 shotgun metagenomic datasets (Suppl. Table  S1 ) from fecal samples of humans (Côte d'Ivoire (CIV), n=12, Dem. Rep. of the Congo (DRC), n=12, Denmark (DK), n=24, and Germany (GER), n=24) and non-human apes, including two gorilla subspecies (Gorilla gorilla gorilla, Gabon (GAB), n=8; Gorilla beringei beringei, Uganda (UGA), n=11, and Republic of Congo (CG), n=28), three chimpanzee subspecies (Pan troglodytes verus,  CIV, n=55; P. t. troglodytes, GAB, n=11, and CG, n=18; P. t. schweinfurthii, UGA, n=12) , and bonobos (Pan paniscus, DRC, n=12), we reconstructed a total of 7,700 metagenome-assembled genomes (MAGs) ensuring maximum completeness and low contamination using multiple binning algorithms and dedicated curation and scoring tools (  (Rühlemann et al.
2 x 10 -13 ; Figure  1a ). Comparing individual human populations to African great ape hosts revealed that humans from GER and DRC showed lower means than all NHA group (all PWilcoxon < 0. 05), while humans from CIV and DK exhibited high variance and were found to have significant lower diversity than all NHA hosts (PWilcoxon < 0. 05), except for G. g. gorilla and P. t. troglodytes (PWilcoxon > 0. 05). These two great ape taxa were found to have the lowest library sizes, low numbers of recovered genomes and lowest mapping efficiency. Taken together, this suggests that phylogenetic diversity in these hosts may be biased by lower representation in the reference database and that the reduced diversity could be an artefact of this. Consequently, samples with less than 1 million mapped reads were removed from further analyses.
Sulfur metabolism (map00920) was generally overrepresented in differentially abundant KOs with individual effect directions associating with both groups (QBonferroni < 0. 05; Figure  1j , Suppl. Table  S7 ). NHA associated categories md:M00356, md:M00563 (methanogenesis) and path:map00680 (methane metabolism) clearly indicate a higher abundance of methanogenic archaea. Additionally, we find multiple categories involving ribosomes, including distinct ribosome annotations of archaea, which we could confirm using the pangenome distribution of these KOs (e. g. 92. 9% of SGBs with K02866 [large subunit ribosomal protein L10e] belonging to the domain Archaea). In humans, we find enrichments in categories covering antimicrobial resistance (path:map01501, br:ko01504), bacterial mobility (path:map02030, path:map02040, br:ko02035), biofilm formation (path:map02026) and prokaryotic defense systems (br:ko02048), suggesting a generally higher abundance of virulence genes in human microbiomes, independent of geography.
2%), with no significant differences between the subgroups (PKruskal-Wallis = 0. 2). The analysis was conducted at the family-level, as higher taxonomic ranks would increase clade-specific functional biases. To account for betweenfamily functional differences, functional differences of SGBs associated with Europeans were first analyzed within microbial families using Fisher's exact test and subsequently subjected to unweighted meta-analysis using Z-scores to leverage shared signals. We identified 167 enriched and 30 depleted KOs in pangenomes associated with the European populations (QMeta,Bonferroni<0. 05; Suppl. Tables  S8  and S9 ). Using higher-level KEGG annotations (modules, pathways, BRITE hierarchies), we found that 11 of these annotations were overrepresented in the dataset (QBonferroni < 0.
, 2008). OMFs and MFS form transmembrane complexes for the transport of a large variety of solutes  (Yen et al. , 2002) , including e. g. carbohydrates, metal ions, amino acids, and export of toxic compounds  (Davidson and Chen, 2004; Lee et al. , 2014). How these potential adaptations relate to NHA hosts is unclear, however, they might indicate adaptation to a diverse diet  (Ejby et al. , 2016; Tsujikawa et al. , 2021)  or metabolism of plant-derived xenobiotics  (Rodríguez-Daza et al. , 2021). Prevotella represented the largest genus-level clade in the dataset (n=212, 3. 7% of all SGBs). While we found this genus across all host species, it is largely decreased in abundance and prevalence in Europeans. We selected this genus for further analysis to elucidate potential functional mechanisms driving the observed patterns.
Enrichment analysis revealed 126 KOs with distinct prevalence patterns (Q<0. 05; Figure  4b ). The most striking difference was found for the cytochrome bd ubiquinol oxidases subunits 1 and 2 (cydA and cydB), which were found in 101 of 114 human-associated Prevotella SGBs (incl. UHGGv2 genomes), but present in only two out of 72 SGBs from NHAs. It is important to note that these prevalence differences are not driven by single lineages within the Prevotella genus that would have distribution ranges restricted to single hosts. Instead, they are observed across multiple sibling clades spanning the entire phylogenetic tree of the taxon (Figure  4c ). Cytochrome bd oxidases are involved in stress responses, most prominently in transiently microaerobic environments  (Giuffrè et al. , 2014). Comparison of the Prevotella species tree (reconstructed using all SGB representatives recovered from the dataset (n = 184)) and the cydA gene phylogeny exhibit widespread incongruencies between their tree topologies (Figure  4c ).
Co-phylogeny is rare in spore-forming microbes and disrupted in humans Patterns of co-phylogeny between host and microbes can result from close interaction, or even interdependence in extreme cases, and congruent metabolic pathways from coevolutionary trajectories. Using stringent selection criteria, we subjected 209 subtrees of the SGB phylogeny for co-phylogeny analysis (see Methods). The subtrees spanned 46 families and 945 (28. 8%) SGBs present in the dataset in addition to 77 SGBs from the UHGGv2 catalog. We used a Mantel-test based framework and permutation to detect co-phylogeny signals (  (Hommola et al. , 2009) , see Methods for details). When defining co-phylogeny candidates based on a mean P-value < 0. 05 across all permutations, 56 of 209 subtrees (26. 8%) qualified as exhibiting co-phylogenetic patterns (Suppl.
Signals of cophylogeny suggest a strong association and possible adaptation with the host and the reduction of genome size and gene content are expected patterns connected to this process  (Toft and Andersson, 2010)  which has previously been shown also for microbes with codivergence patterns in human population  (Suzuki et al. , 2022). To explore whether these processes could be detected in our dataset, we analyzed genome size and gene count, as well as 43 microbial traits inferred from genome-level annotations for signals in association with co-phylogeny using logistic regression, while controlling for phylogenetic relatedness (see Methods; Suppl. Table  S15 ). Out of 45 analyzed traits, 13 were found significantly depleted in clades exhibiting co-phylogeny signals (QBonferroni < 0. 05; Figure  5d ; Suppl.
, 2020; Hildebrand et al. , 2021; Moeller et al. , 2016; Nayfach et al. , 2016). We did not find any negative correlation between spore-formation ability and cophylogeny patterns (Q = 1, Suppl. Table  S16 ), however the annotation of this trait was restricted to only two phyla (Firmicutes and Firmicutes A) and was also rare within these clades, being found in only 54 SGBs across the dataset. As such, whether these results contradict previous findings cannot be concluded in this analysis and warrants future focused analyses. We found 67 out of 157 SGBs in Bacteroidaceae within subtrees with cophylogeny signals (Copylogeny-Ratio = 43. 5%), consistent with previous findings based on gyrB amplicon data  (Moeller et al. , 2016). However, no evidence for strict co-phylogeny was found in Bifidobacteriaceae (nSGB=10 in the analysis, none with co-pyhlogeny signals), which is inconsistent with findings from the same report  (Moeller et al.
Discussion Here, we present the largest curated dataset of fecal metagenomes derived from wild African great apes and human populations. For this, we surveyed and reconstructed high-quality microbial genomes from the feces wild non-human apes, including gorillas (Gorilla gorilla gorilla; Gorilla beringei beringei), chimpanzees (Pan troglodytes verus; P. t. troglodytes; P. t. schweinfurthii), and bonobos (Pan paniscus) as well as human populations from Africa and Europe. We identified signals of phylosymbiosis across the included hominids, indicating a conserved evolutionary relationship of microbial communities with their host species. Moreover, by employing a comparative approach, we found extensive changes of microbial taxonomic and functional abundances across the intestinal microbiota of NHAs and humans.
Previous studies have pointed to "Western" lifestyles as an important factor influencing the intestinal microbiota in humans. Within our human sample population, we were able to confirm differential signals of prokaryotes associated with the European human populations. Importantly, using a comparative dataset of great ape taxa showed that microbial clades lost in Europeans in comparison to African human populations are also found in wild great ape populations. Thus, we suggest that the loss of these taxa might be regarded as the departure from a natural divergence trajectory since their last shared ancestor, cumulating in a mass extinction event of evolutionary conserved members of hominid-associated gut microbiota. While it is tempting to link these changes to industrialization (as previous studies have done), there are many differences between these human populations (e.
, 2016)  or for microbiota insufficiency syndrome (J. L. Sonnenburg and Sonnenburg, 2019) , which seems to be partly driven by changes associated with Western lifestyles. Additional research and large-scale strain collections for Prevotella are needed for an in-depth analysis and evaluation of this diverse taxonomic group with regard to host health and its role in inflammation. Such research must consider Prevotella spp. as members of a complex consortium of interacting microorganisms and as, we argue, a potential target for pre-and probiotic intervention in chronic inflammatory disorders. Lastly, we leveraged our catalog of high-quality metagenome-assembled genomes from hominid fecal samples together with existing data to investigate co-phylogenetic patterns across the sampled hosts. Overall, co-phylogeny showed highly clade-specific enrichments and depletions.
In addition, human-derived MAGs were found significantly less often among co-phylogenetic groups than MAGs from NHAs. Since we included human-derived data from global reference datasets  (Almeida et al. , 2021) , this effect is unlikely to be an artifact of nonexhaustive coverage of human microbiome members. We found several microbial traits depleted among bacteria with cophylogenetic patterns. Among these were, as expected, reduced genome sizes and gene counts, as well as susceptibility to bile and utilization of multiple simple carbohydrates. We hypothesize that these depletions mirror the specialization of microbes to colonize the hominid gut and utilize host-derived complex carbohydrates. Our study has limitations. The co-phylogeny analysis relies heavily on genome-sequences recovered from shotgun metagenomic sequencing (MAGs), which are potentially contaminated and incomplete, which could bias tree structure and thus, co-phylogeny estimates, and also the can potentially under-(or over-)estimate the functional capacities of recovered microbial genomes.
In the presented dataset, species-level sharing of MAGs between host genera was low (1. 68%; n = 30 out of 1,787 reconstructed SGBs, not including  Manara et al. and UHGGv2). SGBs were defined on 95% average nucleotide identity, a measure generally regarded as appropriate  (Jain et al. , 2018) , but it is nevertheless prone to clade specific biases, potentially further influenced by altered speciation dynamics in association with (evolutionary) changes in host lifestyle  (Lawrence and Retchless, 2009) , i. e. previously demonstrated increases in horizontal gene transfer (HGT) within individual microbiomes  (Groussin et al. , 2021). Accounting for these potential biases, we relaxed the threshold to a shared genus-level annotations for subgroups to be included in the co-phylogeny testing, while keeping the number of tested subphylogenies to a minimum through the definition of stringent inclusion criteria (see Methods).
Data processing, assembly and metagenomic binning Raw sequencing FastQ files were quality controlled and preprocessed using the BBMap software suite ("BBMap," n. d. ). Host reads were removed using bbmap. sh. A masked human reference database ("Introducing RemoveHuman," 2014) and a lenient mapping threshold of 95% identity was used to account for a broader host range to also capture host contamination from the Pan and Gorilla host. Metagenomic contigs were assembled with metaSPAdes and contigs >= 2000 bp were retained  (Bankevich et al. , 2012). Reads were mapped to the contigs of the respective samples using Minimap2  (Li, 2018) , converted to BAM files with Samtools  (Li et al. , 2009)  and used to estimate per-contig mapping depth with the jgi_summarize_bam_contig__depths binary from the MetaBAT2 binning tool  (Kang et al.
, 2017). Taxonomic annotations were performed using the GTDBtk (v2. 1) and GTDB release 207v2  (Chaumeil et al. , 2022; Parks et al. , 2022). For SGBs without genus-and/or species-level assignments, the SGB ID was used as taxonomic label. GTDBtk marker gene alignments were used to generate a phylogenetic tree of all SGB representatives using the respective "infer" function of the GTDBtk. All data processing scripts are available online: https://github. com/mruehlemann/greatapes_mgx_scripts.
Pangenome catalog creation, annotation and analysis All MAGs underwent calling of coding sequences using prodigal (v2. 6. 3)  (Hyatt et al. , 2010). Protein sequences were clustered based on 95% similarity using MMseqs  (Hauser et al. , 2016; Steinegger and Söding, 2017)  and annotated using the emapper. py script of the eggNOGmapper v2  (Cantalapiedra et al. , 2021)  annotation tool with the eggNOG 5. 0 reference database  (Huerta-Cepas et al. , 2019). MAG level functional profiles based on KEGG Ortholog annotations were collapsed into SGB-level pangenomes for each host genus (Homo, Gorilla, and Pan). In the case that no MAGs of an SGB were recovered from a given host genus, functional profiles were inferred from MAGs across the other host groups, accounting for host-specificities in the inferred accessory genomes/functions by considering a function to be present if it was present in all host-specific pangenomes of the respective SGB with MAGs recovered from the metagenomic data.
Calculation of microbial clade and functional abundances All downstream data processing and statistical calculations were performed in R v4. 2 (R Core Team, 2022) and using the tidyverse library  (Wickham et al. , 2019). Per-sample contig abundances for the SGB representatives from Salmon were used to estimate SGB abundances. Salmon output includes total mapped reads per contig and mapping reads adjusted for library size and total sequencing depth as transcripts per million (TPM), a measure from the transcriptomics field which can be directly transferred to metagenomic libraries. Individual contig coverages were calculated from the number of mapped reads and the effective lengths of the Salmon mapping output, considering contigs with > 10% coverage as present. An SGB was considered present when at least 20% of its total length was in contigs marked as "present" and if at least 1,000 total reads and 250 TPM mapped to it.