dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Availability We made raw sequencing data for all metagenomes publicly available (https://www. mg-rast. org/) (see Supplementary Table  1  for MG-RAST IDs). In addition, we have made available as anvi'o profiles (1) individual co-assemblies and the final set of metagenome-assembled genomes (https://doi. org/10. 6084/ m9. figshare. 11955060), which offer reproducible access to detection and coverage statistics of each genome across metagenomes, and (2) pangenomes (https://doi. org/10. 6084/m9. figshare. 11968674 for Figure  1B  and https://doi. org/10. 6084/ m9. figshare. 11968692 for Figure  1C ), which offer reproducible access to functions and sequences of each gene cluster and their distribution across genomes. The raw sequencing data for 16S rRNA gene amplicon sequences reported in this study are also publicly available (MG-RAST mgp82768).
Targeted PCR was then performed on isolates cultivated anaerobically from the same fecal samples corresponding to MAGs 1-10. We subcultivated an isolate that was positive for all 4 validated primer sets for each MAG and obtained 18 cultivars that appeared to be MAG 1. We were unable to establish cultivars for MAGs 2-10, which we attributed to the significantly greater relative abundance of MAG 1 relative to MAGs 2-10, giving advantage to MAG 1 in outcompeting other strains under selective growth conditions. Whole-genome sequencing (WGS) was performed on these 18 isolates. Whole genomes for each isolate were assembled with PATRIC (Pathosystems Resource Integration Center) using raw sequence data. Although the top 2 National Center for Biotechnology Information BLAST database hits for MAG 1 were Bacteroides caccae ATCC41385 and Bacteroides caecimuris I48, the top 2 BLAST hits for WGS-assembled genomes for the 18 isolates were Bacteroides thetaiotaomicron 7330 and B thetaiotaomicron VPI-5482.
Shotgun Metagenomics and Genome-Resolved Metagenomics (Binning) Scaffolds were binned as follows: (1) An anvi'o contigs database was generated from assembled scaffolds using the anvi-gen-contigs-database program, during which Prodigal, 1 version 2. 6. 3 with default parameters identified open reading frames and HMMER, 2 version 3. 1b2, identified genes matching to bacterial single-copy core genes. 3 (2) The program anvi-import-taxonomy-for-genes imported genelevel taxonomic annotations predicted by Centrifuge 4 against the National Center for Biotechnology Information's nucleotide database into anvi'o. (3) Short reads were mapped to the scaffolds for each metagenome using Bowtie 2, 5 version 2. 0. 5, and the recruited reads were stored as BAM files using SAMtools. 6 (4) anvi-profile was used to process each BAM file for each sample to estimate the coverage and detection statistics of each scaffold, and combined mapping statistics were stored in an anvi'o merged profile database using anvi-merge.
Pangenome Analysis To compute a pangenome of MAGs and isolates, we used the anvi'o pangenomic workflow as previously described. 8 riefly, this approach consists of 2 major steps: (1) generating an anvi'o genome database using the program anvi-gen-genomes-storage (version 6. 1) to store DNA and amino acid sequences, as well as functional annotations of each gene in each genome, and (2) computing the pangenome using the program anvi-pan-genome. During the second step, gene clusters are identified by first determining sequence similarities between genes with a reciprocal search using the National Center for Biotechnology Information's BLASTP program  9  and later resolving the network of associations using the Markov Cluster algorithm 10 with default parameters. We also used the program anvi-compute-genome-similarity to calculate the average nucleotide identity between genomes, which relied on PyANI,  11  and import the heatmap into the final pangenome.
Results were then visualized in the anvi'o interactive interface using the program anvi-display-pan, and anvisummarize gave access to gene cluster distribution patterns across MAGs and isolates as well as gene sequences in gene clusters. The URL http://merenlab. org/p describes the details of the anvi'o pangenomics workflow. The anvi'o pan database that gives reproducible access to gene clusters across genomes is accessible at https://doi. org/10. 6084/m9. figshare. 11968674 for Supplementary Figure  1A  and https://doi. org/10. 6084/m9. figshare. 11968692 for Figure  1B.
Fecal DNA Extraction and 16S Ribosomal RNA Gene Amplicon Sequencing Fecal samples were harvested and rapidly frozen at -80 C. Fecal DNA extraction and sequencing were performed using previously reported protocols. 18 Briefly, the V4 region of the 16S rRNA gene was amplified following the Earth Microbiome Project protocol (http://www. earthmicrobiome. org/protocols-and-standards/16s/), and sequences were obtained by Illumina MiSeq at the Argonne Sequencing Center. The raw sequencing data were analyzed by QIIME2 2018. 8 with DADA2. 19, 20 Samples with fewer than 4000 sequences were excluded from the analyses. Taxonomy was assigned using the taxonomy classifier trained on the Greengenes 13_8 99% ASVs that is provided by the QIIME2 development team (https://docs. qiime2. org/2019. 4/data-resources/).
=Figure 1. 1 Figure 1. Identification and cultivation of a key commensal Bacteroides strain that is important for host immune development. (A) The detection rates of bacterial MAGs in fecal samples obtained from dams and pups over time. Each column represents a single MAG, and each row represents an individual sample. The darker the cell, the greater the relative abundance. Metadata for each sample are presented to the right of the heatmap. Colored bars at the bottom of the heatmap represent the MAG annotations at the phylum level. Green arrows indicate CPZ-sensitive MAGs that are residents of IL10-KO dams and their NT offspring that did not develop colitis but which emerged later in life in the CPZ group offspring that develop colitis. (B) Pangenome analysis and assessment of AN) for 18 assembled genomes based on WGS for 18 isolated cultivars compatible with MAG 1, shown together with MAG 1 and the 4 most similarly related complete genomes registered in the BLAST database.
Acknowledgements The authors acknowledge funding from the  Wenner-Gren Foundations , the  Swedish Research Council  (including Reg. No. 621-2014-5599 ), the  Swedish Foundation for Strategic Research , the  ÅForsk foundation ,  National Research Foundation of Korea (NRF)  grant funded by the  Korea government (MSIT)  (No. 2020R1A2C1102558 ) and  Swedish Government Strategic Research Area in Materials Science on Functional Materials at Linko ¨ping University  ( Faculty Grant   SFO-Mat-LiU o 2009 00971 ).
Deformation Tests Our creep experiments were performed in a uniaxial compression apparatus (Autograph AG-X) (Appendix A) with an integrated furnace under non controlled atmosphere conditions (free air) (Figure  A1 ). The axial force was measured by a highly sensitive load cell directly attached to the loading rod, while displacement was measured at the crosshead of the apparatus. All our mechanical , that is, force-displacement data were obtained at a stepped load (i. e. , applying multiple stresses) for a constant temperature. We collected force-displacement-time data every second. Our load measurement is accurate within ±0. 4% of the applied force and displacement rates are within ±0. 1% of the set speed  (Nakakoji et al. , 2018). The compressive force data were converted to differential stresses by assuming a change in area with uniform compression and constant sample volume.
It ensures negligible grain growth during thermal etching. In order to observe the microstructures and determine the average grain sizes, all the samples were examined under a scanning electron microscope (SEM) equipped with a field emission gun (JEOL 7001F) at Nano-Manufacturing Institute, The University of Tokyo. Additionally, transmission electron microscopy (TEM) was carried out to observe the microstructure and the occurrence of any melt at the grain boundaries and triple junctions (Appendix B). We conducted the deformation experiments as well as the aggregate syntheses under dry conditions, so that the aggregates had little chance of incorporating water. We confirmed the "dry" state of the aggregates using Fourier transform infrared spectroscopy (FT-IR) (Appendix C; Figure  A3 ). We used the freely available "Image J" (NIH) software to carry out the grain size analysis from the SEI images.
It is clearly seen from this plotting that some of the earlier studies  (Bystricky & Mackwell, 2001; Hier-Majumder et al. , 2005)   Although DIM concluded the p value as 3, it seems that the value of 2 can explain their synthetic diopside mechanical data and complement our Di (Fo) data at the larger grain-size range (Figure  7b ). In fact, DIM proposed the p value based on analysis of pure diopside end-member product like ours using synthetic diopside glass samples. Unfortunately, it is difficult to recognize each of those data from their table. Therefore, our analysis of DIM data is limited to their accessible data, which leads to a different conclusion on the p value (Figure  7b ). Similar to DIM, HM also preferred p = 3, assuming grain boundary diffusion creep. Considering their limited grain size range and scatter (Figure  7b ), we have to judge their data for determining the p value as inadequate.
Acknowledgments  S. Ghosh  acknowledges the International Research Promotion Office of the  Earthquake Research Institute, The University of Tokyo  for the Visiting Post- doc Invitation Program  and supporting this study. We are grateful to  K. Yabe  and  A. Yasuda  for the FT-IR analysis. This study would not be possible without the help of  A. Okamoto ,  H. Yamauchi ,  A. Takeuchi ,  S. Ohtsuka , and  N. Kim. S. Ghosh  is thankful to  A. Laik  for color coding the Figure 5. We thank  G. Dresen ,  A. Dimanov , and  A. Tommasi  for their constructive reviews. We are also thankful to  Yves Bernabe  and  Qin Wang  for handling this manuscript. A portion of this work was conducted at the  Center for Nano Lithography & Analysis of the University of Tokyo , supported by the  Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan  (Grant Number  JPMXP09A19UT0014 ).
The maximum marking speed of our scanning head is 1 m/s with a repeatability < 2 µrad. A commercial laser machining software (LaserDesk, Scanlab) is used to control and generate the movements of the mirrors and ensure the synchronization with the laser irradiation. The laser power was calibrated with a power calorimeter (Gentec, UP55C-2. 5KW-HD-D0). For the experiments conducted under vacuum atmosphere (on UO2 samples), the laser beam is focused in a vacuum chamber (X40K, Neyco), equipped with a UV fused silica window (WG41050-C, Thorlabs) which has an antireflection coating for the laser wavelength. The operating pressure is close to 10 mbar. The experiments on graphite were carry on under air. The main process parameters for ablating and cutting graphite sample with pulse laser irradiation are shown schematically in Figure  1 (b).
III. MODELING Simulations are conducted with the commercial software COMSOL, which is based on the Finite Element Method, in axisymmetric configuration, with a two dimensional meshing, to go further in the investigation and understand the laser ablations mechanisms. Heat transfer by conduction associated with deformed geometry and ray-tracing modules are employed for the analysis of the thermal effects, material evaporation and laser propagation on the fabricated structures. The heat transfer model relies on several assumptions. First, the graphite is considered homogeneous and isotropic. Second we take into account the heat transfers by conduction combined with thermal radiation to take into account losses at the surface (convection is neglected). Third, as the energy is absorbed on the surface, there is no heat source in the material but an incoming heat flux on the boundary, deduced from a ray tracing approach.
Data source VigiBase (https://www. who-umc. org/vigib ase/vigib ase/) is the unique World Health Organization (WHO) global database of individual case safety reports. 29 This database is maintained, deduplicated, and deidentified by Uppsala Monitoring Centre (Uppsala, Sweden). VigiBase is the world's largest pharmacovigilance database, with over 23 million reports of suspected adverse drug reactions gathered from national pharmacovigilance systems and continuously updated. More than 130 countries, over 5 continents submit spontaneous safety reports in this database. Information concerning the reporter, the patient, the suspected and concomitant drugs, and the suspected adverse drug reactions and their seriousness, are reported for each case. Reactions are coded using the hierarchical Medical Dictionary for Regulatory Activities (MedDRA; https://www.
Data extraction and analysis Reports including an mRNA COVID-19 vaccine as the suspected drug in the adverse reaction have been extracted. From that, cases of inflammatory heart reactions (i. e. , myocarditis, myopericarditis, pericarditis, and pleuropericarditis), were retrieved using the ad hoc high-level terms from MedDRA, "noninfectious myocarditis" and "noninfectious pericarditis," coded as reactions. Cases included in this study were registered up to June 30, 2021, in VigiBase. For each report of interest, the following data were extracted or assessed: demographic features, continent of reporting, type of reporter, month of reporting, patient sex, patient age, time to reaction onset, seriousness, reaction outcome, and type of heart inflammatory reaction. Seriousness was defined, according to the WHO, as the occurrence of death, life-threatening adverse event, inpatient hospitalization or prolongation of an existing hospitalization, significant disability, or requirement of intervention to prevent any of these.
Disproportionality analysis Disproportionality analyses, also called case-non-case studies, are similar to case-control studies but for the purpose of pharmacovigilance studies. These studies are nested in a database of spontaneous reports to assess possible disproportionality in reporting. 3] [34]  This statistical approach has shown its value to provide an estimation of relative risks of adverse drug reaction, that are correlated in most cases with results from meta-analyses of clinical trials. 35 o assess an association between inflammatory heart reactions after mRNA COVID-19 vaccines and patient age or sex, we performed a disproportionality analysis among different patient groups. This analysis was realized in the cohort of mRNA COVID-19 vaccine reports. Characteristics of interest are patient sex and age groups, specially 12-17 years, 18-29 years, and over 30 years, as reported by safety signals of concern.
Demographics and characteristics of cases As of the end of June 2021, of a total of 26,258,646 reports registered in the worldwide spontaneous report database VigiBase, 716,576 reports were related to an mRNA COVID-19 vaccine as a suspected drug, including 495,493 with tozinameran and 221,368 with elasomeran (including 285 reports with both vaccines). Among them, 2,277 cases of inflammatory heart reactions were retrieved, such as 1,241 (54. 5%) myocarditis, 851 (37. 4%) pericarditis, 167 (7. 3%) myopericarditis, and 18 (0. 8%) pleuropericarditis (Table  1 ). Cases were largely reported from North America (63. 4%) and Europe (35. 0%). Of the cases, 1,655 (72. 7%) were reported with tozinameran and 622 (27. 3%) with elasomeran. Patients were of the younger age bracket (median age 33, (21-54)) mostly from the group between 18 and 29 years old (704, 30.
However, in the current context of a highly communicated risk at a global level, such as the myocarditis risk as of June 2021, the reporting rate may be relevant to approximate incidence. Another limitation is the quality of the data reported in pharmacovigilance databases, that could result in some cases in misdiagnosis. However, it appears not likely that this precludes our analysis according to patient age and sex, which was consistent after considering only reports originating from healthcare professionals. Furthermore, VigiBase, covering more than 90% of the world's population, provides a unique opportunity to analyze rare adverse events at a global scale. Disproportionality analysis on VigiBase has proven its value in detecting increased risk of events. 32 Our study confirmed the value of disproportionality analyses in relative risk estimation among subgroups of patients.
ACKNOWLEDGMENTS The authors would like to thank all the healthcare workers and the French Pharmacovigilance Network involved in the current large immunization campaign and in its safety monitoring, VigiBase is a fully deidentified database maintained by the Uppsala Monitoring Center (UMC). The authors are indebted to the National Pharmacovigilance centers that contributed data. Information from VigiBase comes from a variety of sources, and the probability that the suspected adverse effect is drugrelated is not the same in all cases. The information does not represent the opinion of the Uppsala Monitoring Center (UMC) or the World Health Organization and only reflects the authors' opinion. According to VigiBase access rules, no specific ethical approval is needed. VigiBase access is granted to national and regional pharmacovigilance centers such our teams.
All biomass burning-related factors were in good agreement, with a typical diel profile and a night-time increase linked to residential heating. If PMF applied to ACSM data showed good agreement with other PMF outputs corrected from dust-related factors (coarse PM), discrepancies were observed between individual POA factors (traffic, biomass burning) and directly comparable SOA factors and highly oxidized OA. Similar secondary organic carbon (SOC) concentrations (3. 3 ± 0. 1 µg m -3 ) were obtained from all approaches, except the SOA-tracer method (1. 8 µg m -3 ). Associated uncertainties ranged from 14 to 52% with larger uncertainties obtained for PMF-chemical data, EC-and SOA-tracer methods. This latter significantly underestimated total SOA loadings, even including biomass burning SOA, due to missing SOA classes and precursors.
Subjectivity in the choice of the source apportionment method, final solutions and the assumptions applied, might have strong impacts on the results and conclusions obtained. Comparison of results obtained from different approaches to apportion POA/SOA fractions during such events would highlight discrepancies and uncertainties between each approach and further understanding of OA sources. In this work, POA and SOA fractions have been resolved using various source apportionment methods, applied to different offline/online datasets collected from a short-term intensive campaign in the Paris area (France) during a springtime PM pollution event. PMF analyses have been conducted using extensive filter-based chemical speciation data, as well as OA mass spectra obtained from online aerosol chemical speciation monitor (ACSM) and offline filter-based aerosol mass spectrometry (AMS) measurements.
Online instrumentation PM10 (TEOM 1405F, Thermo), NOx (T200UP, Teledyne API), O3 (T400, Teledyne API) and black carbon (BC) (aethalometer AE33, Magee Scientific) concentrations were measured at 15, 1, 1 and 1-min time resolutions, respectively. In addition, meteorological parameters such as temperature, relative humidity (RH), wind direction, and wind speed were also measured at 1-min time resolution. The ACSM (Aerodyne Research) allowed for the measurement of major submicron (PM1) non-refractory (NR) chemical species at about 30-min time resolution. Details on these measurements can be found elsewhere  (Petit et al. , 2017; Zhang et al. , 2019). The ACSM dataset used in this work has already been reported previously  (Srivastava et al. , 2019).
Source apportionment approaches Each source apportionment methodology (EC-tracer, SOA-tracer and PMF analyses performed on the different datasets) is briefly discussed below. Details on the calculation of the overall uncertainties (measurement + model) linked to POC and SOC estimates using these approaches are presented in the SM. Limitations linked to these approaches are also briefly discussed in the SM and also explained elsewhere  (Srivastava et al. , 2018b). Note that for measurements, the uncertainties linked to the sampling were not considered. For filter-based methodologies, the uncertainty on the sampling volume collected would have been the same for all the methods and usually accounts for a minor part of the measurement uncertainty  (Ringuet et al. , 2012; Albinet et al. , 2013; Albinet et al.
PMF-based approaches Detailed information on PMF principle can be found elsewhere  (Paatero and Tapper, 1994; Paatero, 1997). Briefly, this receptor model resolves factor profiles and contributions from a time series of observations using weighted least-squares fitting approach, where the weights are adjusted according to measurement uncertainties. The choice of the optimal solution is notably based on the minimization of the residuals obtained between modeled and observed input species concentrations. The U. S. Environmental Protection Agency (US-EPA) PMF 5. 0 toolkit has been used to perform the source apportionment on the PM10 filter chemical dataset (including inorganic species and metals, along with EC, OC and organic markers). As detailed in  Srivastava et al. (2018a) , OC concentrations obtained for each relevant factor were then used in this study (Figs.
Number of distinguished POA and SOA classes The identification of each of these factors is summarized in Table  1  and their contributions to total OC in PM10 is shown in Fig. 2. As defined, the EC-tracer method resulted in the estimation of 1 primary and 1 secondary factor. The SOA-tracer method allowed for the quantification of 5 different SOA fractions corresponding to the 5 categories of SOA markers used. PMF analysis conducted on the chemical dataset (PMF-chemical data) led to the discrimination of 10 different OA factors, while analyses on the offline AMS and ACSM datasets (PMF-offline AMS and PMF-ACSM) allowed the identification of 6 and 4 OA factors, respectively. Note that, as ACSM measurements consider only the PM1, the difference between OC in PM10 (from filter measurements) and in PM1 was affected by coarse OC (Fig.
2 ). Combustion-related factors highly influenced by primary organic molecular markers (for PMFchemical data) or by hydrocarbon mass fragments (for PMF-offline AMS and PMF-ACSM) were attributed to the POA fraction. The factors mainly influenced by secondary markers or oxygenated mass fragments were ascribed to the SOA fraction. Dust-related OA factors (retrieved from PMF-chemical data as well as PMF-offline AMS) could be linked to both primary and secondary aerosols due to soil abrasion, resuspension and/or coagulation processes and to condensation of semivolatile organic species onto mineral dust particles. The dust factor identified by PMF-chemical data contained a significant amount of EC (Fig. S2 ), along with metals (mainly crustal) and cations (Ti, Ca 2+ , Mg 2+ , Cu and Mn), with negligible amount of secondary species present in the factor profile.
Comparison of the POA factors Fig. 3  shows the primary OC (POC) concentrations obtained from the different methodologies used for POA apportionment. On average, a good agreement between the outputs from EC-tracer method, PMF-chemical data, and PMF-offline AMS was observed (Fig. 3b ). Averaged PM10 POC concentrations obtained from these three methodologies ranged between 3. 2 and 3. 7 µg m -3 representing ~ 50% of the total PM10 OC. Time series were rather similar (datasets normally distributed, paired t-test; p-values > 0. 05) except between PMF-chemical data and PMF-offline AMS, as shown in Table  S6a  and Fig. 3a. However, significant disagreements can be observed during certain days (e. g. 10 th , 16 th , 19 th March) when the PMF-chemical data and EC-tracer showed high concentrations while both other approaches showed minimum levels.