dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability Patient-related sequencing data are available on dbGaP: phs000645. v4. p1. All other sequencing data are available on GEO: GSE122600 and GSE125046. The source data underlying Figs. 1c, g, h; 2c-f; 3a-e; 4b, c, j-l; 5b-f, h and Supplementary Figs. 1a; 2d, f; 3ac; 5a and c are provided as a Source Data file. All data is available from the authors upon reasonable request.
We present a global likelihood function in the space of dimension-six Wilson coefficients in the Standard Model Effective Field Theory (SMEFT). The likelihood includes contributions from flavour-changing neutral current B decays, lepton flavour universality tests in charged-and neutral-current B and K decays, mesonantimeson mixing observables in the K, B, and D systems, direct CP violation in K → ππ, charged lepton flavour violating B, tau, and muon decays, electroweak precision tests on the Z and W poles, the anomalous magnetic moments of the electron, muon, and tau, and several other precision observables, 265 in total. The Wilson coefficients can be specified at any scale, with the one-loop running above and below the electroweak scale automatically taken care of. The implementation of the likelihood function is based on the open source tools flavio and wilson as well as the open Wilson coefficient exchange format (WCxf) and can be installed as a Python package.
The main aim of the present work is thus to provide a likelihood function that also takes into account a large number of observables in flavour physics, with a focus on the ones that are relevant in models motivated by the anomalies recently observed in B decays based on the b → cτ ν and b → sµµ transition. Our results build on the open source code flavio  [23] , that computes a large number of observables in flavour physics as a function of dimension-6 Wilson coefficients beyond the SM and contains a database of relevant experimental measurements. To incorporate constraints beyond quark flavour physics, we have also implemented EW precision tests, lepton flavour violation, and various other precision observables in flavio. By using open source software throughout, we hope our results can serve as the basis for a more and more global SMEFT likelihood emerging as a community effort.
For decays of particles without b flavour, we match to the appropriate 4-or 3-flavour effective theories. The Python package to be described in section 5 also allows to access a pure WET likelihood. In this case the constraints in the first category are ignored. The complete tree-level matching from SMEFT onto WET  [10, 11]  as well as the one-loop running in SMEFT  [6] [7] [8]  and WET  [12, 13]  is done with the wilson package  [14]. In appendix D, we list all the observables along with their experimental measurements and SM predictions.
Usage The global likelihood is accessed via the Python package smelli (SMEFT likelihood). Given a working installation of Python version 3. 5 or above, the package can be installed with the simple command § ¤ python3 -m pip install smelli --user ¦ ¥ that downloads it from the Python package archive (PyPI) along with all required dependencies and installs it in the user's home directory (no administrator privileges required). The source code of the package can be browsed via a public Github repository  9. As with any Python package, smelli can be used as library imported from other scripts, directly in the command line interpreter, or in an interactive session. For interactive use, we recommend the Jupyter notebook 10  that runs in a web browser. In all cases, the first step is to import the package and to initialize the class GlobalLikelihood, § ¤ import smelli gl = smelli.
GlobalLikelihood() ¦ ¥ The initialization function takes two optional arguments: • The argument eft (default value: 'SMEFT') can be set to 'WET' to obtain a likelihood in the parameter space of WET rather than SMEFT Wilson coefficients. In this case EWPOs are ignored. • The argument basis allows to select a different WCxf basis (default: 'Warsaw' in the case of SMEFT, 'flavio' in the case of WET). By default, smelli uses the leading logarithmic approximation for the SMEFT RG evolution, since it is faster than the full numerical solution of the coupled RGEs. This behaviour can be changed by setting the corresponding option of the wilson package after importing smelli, e. g. § ¤ import smelli, wilson wilson. Wilson. set_default_option('smeft_accuracy', 'integrate') ¦ ¥ The next step is to select a point in Wilson coefficient space by using the parameter_point method.
The Wilson coefficients must be provided in the EFT and basis fixed in the first step. There are three possible input formats: • a Python dictionary (containing Wilson coefficient name/value pairs) and an input scale, • as a WCxf data file in YAML or JSON format (specified by its file path as a string), • as an instance of wilson. Wilson defined by the wilson package. The variable glp defined above holds an instance of the GlobalLikelihoodPoint class that gives access to the results for the chosen parameter point. Its most important methods are • glp. log_likelihood_global() returns the numerical value of the logarithm of the likelihood minus its SM value ln ∆L, i. e. the logarithm of the likelihood ratio or -∆χ 2 /2 when writing the likelihood as L = e -χ 2 /2. • glp. log_likelihood_dict() returns a dictionary with the contributions to ln ∆L from the individual products in (3).
Conclusions In this paper we have presented a likelihood function in the space of dimension-6 Wilson coefficients of the SMEFT. This function is made publicly available in the form of the Python package smelli, building on the existing public codes flavio and wilson. At present, the likelihood includes numerous observables from B and K decays, EWPOs, neutral meson mixing, LFV and CP violating processes and many more, counting a total of 265 observables. We have demonstrated its validity and usefulness by reproducing various results given in the literature. In passing, we have also pointed out new results, in particular the fact that one of the two possible solutions to the R D and R D * anomalies involving the tensor operator is excluded by the recent Belle measurement of the longitudinal polarization fraction in B → D * τ ν, which is included in our likelihood (see section 4.
Acknowledgements We thank  Wolfgang Altmannshofer ,  Christoph Bobeth ,  Ilaria Brivio ,  Andreas Crivellin ,  Martin Jung ,  Aneesh Manohar , and  Jordy de Vries  for discussions. We thank  Alejandro Celis ,  Méril Reboud , and  Olcyr Sumensari  for pointing out typos. We thank  Martín González-Alonso ,  Admir Greljo , and  Marco Nardecchia  for useful comments. The work of D. S. and J. A. is supported by the  DFG cluster of excellence "Origin and Structure of the Universe". The work of J. K. is financially supported by  NSERC of Canada.
The literature search was performed on only one database (ISI Web of Science within selected subject areas) and the review did not include grey literature. Finally, a meta-analysis was performed by Roca et al. [31] , but it dealt exclusively with birds and amphibians and the authors were interested in only one effect (vocalization adjustment). This report describes the protocol used to develop a systematic map of noise pollution and biodiversity. The systematic map will provide further information on the knowledge currently available on this issue. It will include all the relevant studies (with grey literature) collected after three screening stages. An open-access database will be produced, containing metadata for each study on key variables of interest (species, types of sound, effects, etc.
Search string A scoping exercise was conducted on the "Web of Science Core Collection" database to build-up the search strings. Terms describing the exposure (noise pollution) and the population (biodiversity) were combined in an iterative manner until best performance was obtained. Terms describing effects (outcomes) were not included because the aim of the map is to document the available literature without any a priori restrictions on the types of effects measured in the articles. The search string that produced the highest efficiency (number of hits compared to the test list) is presented below (see Additional file 1 for more details on the process to build the search string). ((TI = (noise OR sound$) OR TS = ("masking auditory" OR "man-made noise" OR "anthropogenic noise" OR "man-made sound$" OR "music festival$" OR ((pollution OR transportation OR road$ OR highway$ OR motorway$ OR railway$ OR traffic OR urban OR city OR cities OR construction OR ship$ OR boat$ OR port$ OR aircraft$ OR airplane$ OR airport$ OR industr* OR machinery OR "gas extraction" OR mining OR drilling OR pile-driving OR "communication network$" OR "wind farm$" OR agric* OR farming OR military OR gun$ OR visitor$) AND noise))) AND TS = (ecolog* OR biodiversity OR ecosystem$ OR "natural habitat$" OR species OR vertebrate$ OR mammal$ OR reptile$ OR amphibian$ OR bird$ OR fish* OR invertebrate$ OR arthropod$ OR insect$ OR arachnid$ OR crustacean$ OR centipede$)).
Comprehensiveness of the search A test list of 65 scientific articles was established (see Additional file 2) and used to assess the comprehensiveness of the search string. The test list was composed of the three groups listed below. 1. Forty relevant scientific articles identified by the review team prior to the review. 2. Eight key articles identified using three relevant reviews: • Brumm  [5]  (two articles); • Cerema  [8]  (three articles); • Dutilleux and Fontaine  [11]  (three articles). 3. Seventeen studies not readily accessible or indexed by the most common academic databases, submitted by subject experts contacted prior to the review (29 subject experts were contacted, 7 responded).
Online publication databases We first listed the databases to which the members of our review team had access, databases that covered ecology and that guaranteed reproducibility (accessibility by researchers from all over the world, advanced search functions, etc. ). The resource limitations weighing on the project did not allow us to cover more than two databases given the number of articles obtained during the scoping exercise. On the basis of the criteria listed above, the two databases below were selected: • "Web of Science Core Collection" on the Web of Science platform (Clarivate). See Additional file 3 for citation indexes included in the "Web of Science Core Collection" to which the review team had access via the team members' institutions. As explained above, the scoping exercise was conducted using this database.
The comprehensiveness value was 92% (61 articles in the test list were indexed in Scopus and 56 were retrieved by the string). Approximately 6000 articles were listed in both databases. One of the articles not indexed in the WOS CC was indexed in Scopus and was retrieved by the search string. Consequently, combining the two databases, the global comprehensiveness value was 93% (61 articles indexed and 57 articles retrieved by the search string). See Additional file 4 for more details on the comprehensiveness values.
Eligibility criteria Article eligibility will be based on the list of criteria detailed in Table  2. The list of all articles will be provided, informing the inclusion/exclusion decisions at the three if possible, be deposited as a. csv file in a data repository such as Zenodo. The final systematic map will include summary figures and tables of the study characteristics. Possible knowledge gaps (un-or under-represented subtopics that warrant further primary research) and knowledge clusters (well-represented subtopics that are amenable to full synthesis by a systematic review) will be identified e. g. by cross-tabulating key meta-data variables in heat maps (e. g. biological groups and outcomes). Based on these results, recommendations will be made on priorities for future research and mitigation of noise pollution.
Comprehensive reference data is essential for accurate taxonomic and functional characterization of the human gut microbiome. Here we present the Unified Human Gastrointestinal Genome (UHGG) collection, a resource combining 286,997 genomes representing 4,644 prokaryotic species from the human gut. These genomes contain over 625 million protein sequences used to generate the Unified Human Gastrointestinal Protein (UHGP) catalogue, a collection that more than doubles the number of gut protein clusters over the Integrated Gene Catalogue. We find that a large portion of the human gut microbiome remains to be fully explored, with over 70% of the UHGG species lacking cultured representatives, and 40% of the UHGP missing meaningful functional annotations. Intraspecies genomic variation analyses revealed a large reservoir of accessory genes and singlenucleotide variants, many of which were specific to individual human populations.
Combining these individual efforts and establishing a unified non-redundant dataset of human gut genomes is essential for driving future microbiome studies. To accomplish this, we compiled and analysed 286,997 genomes and 625,251,941 genes from human gut microbiome datasets to generate the Unified Human Gastrointestinal Genome (UHGG) and Protein (UHGP) catalogues, the most comprehensive sequence resources of the human gut microbiome established to date.
Thereafter, we classified each species representative using the Genome Taxonomy Database 27 Toolkit (Supplementary Fig. 2 ), a standardized taxonomic framework based on a concatenated protein phylogeny representing >140,000 public prokaryote genomes, fully resolved to the species level (see 'Methods' for details on the taxonomy nomenclature used). However, over 60% of the gut genomes could not be assigned to an existing species, confirming the majority of the UHGG species lack representation in current reference databases.
Comparison of species recovered in individual studies We investigated how many of the 4,644 gut species were found in the different study collections in order to determine their level of overlap and reproducibility, as well as the ratio between cultured and uncultured species (Fig. 2a ). The largest intersection was between the collections of MAGs, with the same 1,081 species detected independently in the CIBIO, EBI and HGM datasets, but not in any of the cultured genome studies. By restricting the analysis to genomes recovered from 1,554 samples common to all three MAG studies, we found that 93-97% of species from each set were detected in at least one other MAG collection, and 79-86% across all three (Supplementary Fig. 3a ). Similar level of species overlap was observed when comparing studies on a per-sample basis (Supplementary Fig.
3b ). Further, conspecific genomes recovered from the same samples across different studies shared a median ANI and aligned fraction of 99. 9% and 92. 1%, respectively (Supplementary Fig. 3c ). These results emphasize the reproducibility of the different assembly and binning methods used in the largescale studies of human gut MAGs  16, 17, 19. Importantly, rarefaction analysis indicates the number of uncultured species detected has not reached a saturation point, meaning additional species remain to be discovered (Fig. 2b ). However, these most likely represent rarer members of the human gut microbiome, as the number of species is closer to saturating when only considering those with at least two conspecific genomes. We also investigated the intersection between the three large culture-based datasets: the HBC, CGR and the NCBI (which contains gut genomes from the Human Microbiome Project, HMP  4  ).
Most gut microbial species still lack isolate genomes We found that 3,750 (81%) of the species in the UHGG did not have a representative in any of the human gut culture databases. To extend the search to isolate genomes from other environments or lacking information on their isolation source, we compared the UHGG catalogue to all NCBI RefSeq isolate genomes. We identified an additional set of 438 species closely matching cultured genomes, leaving 3,312 (71%) of UHGG species as uncultured (Supplementary Table  2 ). The phylogenetic distribution of the 4,616 bacterial (Fig. 3a ) and the 28 archaeal species (Supplementary Fig. 6 ) revealed that uncultured species exclusively represented 66% and 31% of the phylogenetic diversity of Bacteria and Archaea, respectively, with several phyla lacking cultured representatives (Fig.
9 million genes from 1,267 gut metagenome assemblies, which we grouped into 7,063,981 protein clusters at 90% protein identity (referred to as IGC-90). Nearly all samples used to generate the IGC were also included in the UHGP catalogue (except for 59 transcriptome datasets), but the latter was generated from a larger and more geographically diverse metagenomic dataset (including samples from Africa, South America and Oceania). The UHGP-90 and IGC-90 resulted in a combined set of 15. 2 million protein clusters, with an overlap of 5. 8 million sequences (Fig. 4b ). This revealed that 81% of the IGC is represented in the UHGP catalogue, with the missing 19% likely representing fragments of prokaryotic genomes <50% complete, viral or eukaryotic sequences, plasmids or other sequences not binned into MAGs.
Functional capacity of the human gut microbiota We used the eggNOG  31  , InterPro  32  , COG  33  and KEGG  34  annotation schemes to capture the full breadth of functions within the UHGP. However, we found that 42. 6% of the UHGP-100 was poorly characterized, as 28. 1% lacked a match to any database and a further 14. 5% only had a match to a COG with no known function (Fig. 4c ). Based on the distribution of COG functions, the most highly represented categories were related to amino acid transport and metabolism, cell wall/membrane/envelope biogenesis and transcription. We further leveraged the set of 625 million proteins derived from the human gut genomes to explore the functional diversity within each of the UHGG species. Protein sequences from all conspecific genomes were clustered at a 90% amino acid identity to generate a pan-genome for each species.
5a ). There was a wide variation in the proportion of core genes between species even among clades with more than 1,000 genomes (Fig. 5b ), with a median core genome proportion (percentage of core genes out of all genes in the representative genome) estimated at 66% (IQR = 59. 6-73. 9%). To distinguish the functions encoded in the core and accessory genes, we analysed their associated annotations. Core genes were well covered, with a median of 96%, 94%, 92% and 69% of the genes assigned with an eggNOG, InterPro, COG and KEGG annotation, respectively (Fig. 5c ). However, the accessory genes had a significantly higher proportion of unknown functions (P <0. 001), with a median of 21% of the genes (IQR = 16. 7-27. 3%) lacking a match in any of the databases considered. Thereafter, we investigated the functions encoded by the core and accessory genes on the basis of the COG functional categories.
Discussion We have generated a unified sequence catalogue representing 286,997 genomes and over 625 million protein sequences of the human gut microbiome. Of the 4,644 species contained in the UHGG, 71% lack a cultured representative, meaning the majority of microbial diversity in the catalogue remains to be experimentally characterized. During preparation of our manuscript, a new collection of almost 4,000 cultured genomes from 106 gut species was released  38  , which will be incorporated in future versions of the resource. As 96% of these genomes were reported to have a species representative in the culture collections here included, we do not anticipate this dataset to provide a substantial increase in the number of species discovered. Nevertheless, our analyses suggest additional uncultured species from the human gut microbiome are yet to be discovered, highlighting the importance and need for culture-based studies.
Furthermore, given the sampling bias towards populations from China, Europe and the United States, we expect that many underrepresented regions still contain substantial uncultured diversity. By comparing recently published large datasets of uncultured genomes  16, 17, 19  , we were able to assess the reproducibility of the results from each study. We show that despite the different assembly, binning and refinement procedures employed in the three studies, almost all of the same species and near-identical strains were recovered independently when using a consistent sample set. These results further increase confidence in the use of metagenome-assembled genomes for the characterization of uncultured microbial diversity. With the establishment of this massive sequence catalogue, it is evident that a large portion of the species and functional diversity within the human gut microbiome remains uncharacterized.
Phylogenetic analyses Taxonomic annotation of each species representative was performed with the Genome Taxonomy Database  27  Toolkit (GTDB-Tk) v0. 3. 1 (https://github. com/Ecogenomics/GTDBTk) (database release 04-RS89) using the 'classify_wf' function and default parameters. To use consistent species boundaries between the genome clustering and taxonomic classification procedures, genomes were assigned at the species level if the ANI to the closest GTDB-Tk species representative genome was ³95% and the alignment fraction ³30%. In this taxonomy scheme, genera and species names with an alphabetic suffix indicate taxa that are polyphyletic or needed to be subdivided based on taxonomic rank normalization according to the current GTDB reference tree. The lineage containing the type strain retains the unsuffixed (valid) name and all other lineages are given alphabetic suffixes, indicating they are placeholder names that need to be replaced in due course.
Taxon names above the rank of genus appended with an alphabetic suffix indicate groups that are not monophyletic in the GTDB reference tree, but for which there exists alternative evidence that they are monophyletic groups. We also generated NCBI taxonomy annotations for each species-level genome based on its placement in the GTDB tree, using the 'gtdb_to_ncbi_majority_vote. py' script available in the GTDB-Tk repository (https://github. com/Ecogenomics/GTDBTk/tree/stable/scripts). Maximum-likelihood trees were generated de novo using the protein sequence alignments produced by the GTDB-Tk: we used IQ-TREE v1. 6. 11 to build a phylogenetic tree of the 4,616 bacterial and 28 archaeal species. The best-fit model was automatically selected by 'ModelFinder' on the basis of the Bayesian Information Criterion (BIC) score.
The Unified Human Gastrointestinal Protein (UHGP) catalogue was generated from the combined set of 625,251,941 CDS predicted. Protein clustering of the UHGP and the Integrated Gene Catalogue (IGC)  5  was performed with the 'linclust' function of MMseqs2 v6-f5a1c  53  with options: '--cov-mode 1 -c 0. 8' (minimum coverage threshold of 80% length of the shortest sequence) and '--kmer-per-seq 80' (number of k-mers selected per sequence, increased from the default of 21 to improve clustering sensitivity). The '--min-seq-id' option was set at 1, 0. 95, 0. 9 and 0. 5 to generate the catalogues at 100%, 95%, 90% and 50% protein identity, respectively. We clustered the IGC solely at a 90% and 50% protein identity as it was originally de-replicated at a 95% nucleotide identity  5. Functional characterization of all protein sequences was performed with eggNOG-mapper v2  54  (database v5.