dataesr/scientific-paragraphs-categorization huggingface dataset:
The initial data collection showed stability of the crystals over days and a diffraction quality to about 2 Å. The datasets used in this paper were collected at LCLS, California during experiment LU50. Xray pulses at 9. 5 keV with a pulse energy of 4 mJ, 30 Hz repetition rate and a duration of around 35 fs were generated and used for X-ray diffraction in the macromolecular femtosecond crystallography (MFX) experimental hutch  (Sierra et al. , 2019) ; the diffraction was recorded on a Rayonix MX340 (Rayonix L. L. C, Evanston, USA) detector. The sample was delivered into the X-ray interaction point with the drop-on-tape method; the detailed method was described by Fuller et. al  (Fuller et al. , 2017). Briefly, crystal slurry in a 1 ml SampleLock syringe was pumped with a syringe pump (KD scientific, Holliston, MA) at a flow rate of 8-9 µl/min through a silica capillary into a 6 µl sample reservoir.
An acoustic transducer transferred crystal containing droplets of 2. 5-4 nl volume onto a Kapton tape, which transported the droplets into the X-ray beam at 28° C and 27% relative humidity with a speed of 300 mm/s, which resulted in the crystals being exposed to the He-environment for about 0. 8 s. The enclosure of the setup was filled with a He-atmosphere with an O2 level below 0. 1 % for the BcR2bMnMn-NrdIhq data collection.
Processing, Structure Determination and Refinement The datasets for BcR2bMnMn-NrdIhq and BcR2bMnMn-NrdIox were processed with cctbx. xfel (Brewster, Young, et al. , 2019; Hattne et al. , 2014; Sauter, 2015)  and DIALS  (Brewster et al. , 2018; Winter et al. , 2018). We performed joint refinement of the crystal models against the detector position for each batch to account for small time-dependent variations in detector position and also corrected for the Kapton tape shadow  (Fuller et al. , 2017). Data was scaled and merged to 2. 0 Å resolution using cctbx. xfel. merge with errors determined by the ev11 method  (Brewster, Bhowmick, et al. , 2019)  based on CC1/2 and multiplicity values and on Rfactors after initial refinement. Data statistics are available in Table  1. Both structures were solved by molecular replacement and refined independently with the PHENIX Suite  (Liebschner et al.
, 2019). The phases were solved with phenix. phaser (McCoy et al. , 2007). A BcR2b-NrdI complex structure (PDB ID: 4BMO  (Hammerstad et al. , 2014) ) was modified by manually removing waters and alternate conformations and used as a starting model. The suggested solutions were in the same space group (C2221) as the starting model with similar unit cell dimensions (Table  1 ) with one complex in the asymmetric unit. The Rfree set of the 4BMO model corresponding to 5% of reflections was assigned to all datasets with phenix. reflection_tools. Restraints of the entry "FMN" of the ligand database provided by phenix were used for the oxidized FMN in BcR2bMnMn-NrdIox. Restraints for the hydroquinone FMN in BcR2bMnMn-NrdIhq were generated with phenix eLBOW  (Moriarty et al. , 2009). The datasets were iteratively refined with phenix.
-from reaching the metal site by obstructing the channel. In the "open" conformation, on the other hand, Glu161 bridges both manganese ions leaving space for three water molecules to connect Mn2 to the water network in the channel between FMN and the metal site. The open conformation of the equivalent glutamate has been observed in structures of di-manganese R2b alone, e. g. from B. cereus, E. coli and Streptococcus sanguinis (PDB ID: 4BMU, 3N37, 4N83) (Fig. 4 C) but also in the R2b-NrdI complex of E. coli (PDB ID: 3N3A) (Fig. 4 D) (Boal et al. , 2010; Hammerstad et al. , 2014; Makhlynets et al. , 2014). Thus, NrdI binding to R2b is not responsible for triggering the open conformation of Glu161 (Glu158 in E. coli and Glu157 in S. sanguinis). It has been hypothesized that the presence of manganese ions in the active site could cause the glutamate to shift to the open conformation as the closed conformation is typically observed in R2b structures containing a di-iron site, e.
The crystals were resuspended and pooled in 2 ml microcentrifuge tubes (Fig. S1). Crystals used to collect the dataset for BcR2bMnMn-NrdIox could be directly loaded into a Gastight SampleLock (Hamilton, Bonaduz, Switzerland) syringe for sample delivery. The crystals used for the BcR2bMnMn-NrdIhq dataset had to be chemically reduced first as described below. Reduction protocolCrystals for the BcR2bMnMn-NrdIhq dataset were chemically reduced before data collection. All following steps were conducted in an anaerobic glove box with O2 below 10 ppm at room temperature. A volume of 900 µl of pooled crystal slurry was gently centrifuged, forming a dense crystal pellet. Of the supernatant 800 µl were carefully removed, collected separately and supplemented with 18 µl of freshly prepared, anaerobic 1 M sodium dithionite (DT).
Acknowledgements We thank  Robert Bolotovsky ,  Iris D. Young  and  Lee J. O'Riordan  for help with data processing and the staff at LCLS and SACLA. We thank  Kristine Grāve  for input and helpful discussions during data analysis and manuscript writing. This work was supported by the  Swedish Research Council  ( 2017-04018  and  2021-03992  to M. H. ), the  European Research Council  ( HIGH-GEAR 724394  to M. H. ), the  Knut and Alice Wallenberg Foundation  ( 20217. 0275and 2019. 0436  to M. H. ), and  National Institutes of Health  grants  GM133081  (to K. D. S. ),  GM117126  (to N. K. S. ),  GM55302  (to V. K. Y. ),  GM110501  (to J. Y. ) and  GM126289  (to J. K. ). A. M. O. , P. A. , and  A. Bu. were supported by  Diamond Light Source , the  UK Science and Technology Facilities Council (STFC) , a jointly funded strategic award from the  Wellcome Trust  and the  Biotechnology and Biological Sciences Research Council  ( 102593  to  James Naismith ), a  Wellcome Investigator Award  ( 210734/Z/18/Z  to A.
M. O. ), and a  Royal Society Wolfson Fellowship  ( RSWF\R2\182017  to A. M. O. ). The DOT instrument used in this research was funded by  Department of Energy (DOE), Office of Science, Office of Basic Energy Sciences (BES) ,  Division of Chemical Sciences, Geosciences, and Biosciences  (to J. K. , J. Y. , and V. K. Y. ). XFEL data was collected under proposal LU50 at LCLS,  SLAC, Stanford, USA , and under proposal 2017B8085 at BL2 of SACLA, Japan. The Rayonix detector used at LCLS was supported by the  NIH  grant  S10 OD023453. Use of the LCLS,  SLAC National Accelerator Laboratory , is supported by the  U. S. DOE, Office of Science, BES , under contract no. DE-AC02-76SF00515.
Growth on different carbon sources whiA is the 4 th gene in an operon of 6 genes that is constitutively expressed during growth (Fig. S3 )  (7). The first gene, yvcI, encodes a Nudix hydrolase that hydrolyses organic pyrophosphates and is considered a housecleaning enzyme  (15, 16). The second gene, yvcJ, encodes a GTPase required for the proper expression of DNA uptake proteins during natural competence  (17, 18). The third gene, yvcK, encodes an UDP-sugar binding protein that is essential for growth under gluconeogenic conditions  (19). crH, the fifth gene downstream of whiA, is a HPr-like protein that participates in catabolite repression as secondary cofactor of the global catabolite regulator CcpA  (20, 21). The final gene, yvcN, is an uncharacterized acetyltransferase (Subtiwiki database  (16) ).
In many bacteria whiA is located adjacent to yvcK and crh (STRING database  (22) ). Possibly, this conserved organization points towards a metabolic function of WhiA. Inactivation of yvcK blocks growth on citrate and result in very poor growth on either fumarate or malate as sole carbon sources  (23). To examine whether the absence of WhiA also affects growth using these carbon sources, we grew a whiA mutant in Spizizen minimal salt medium using either malate, fumarate or citrate as carbon source. To prevent any downstream effects, a marker-less whiA mutant was used, containing a stop codon at the beginning of the gene (strain KS696  (7) ). As shown in Fig. 2A , the whiA mutant was able to grow in the different media with a growth rate similar to that of the wild-type strain, indicating that WhiA and YvcK work in different pathways.
In brief, 400 μl of the sample was mixed with 200 μl of a sodium hydrogen phosphate buffer (0. 2 M, pH 7. 0) to avoid chemical shifts due to pH, which was made up with 50 % D2O. The buffer also contained 1 mM trimethylsilyl propanoic acid-d4 (TSP) which was used for quantification and also as a reference signal at 0. 0 ppm. To obtain NMR spectra, a 1D-NOESY pulse sequence was used with a presaturation on the residual HDO signal. A total of 64 FID scans were performed with 600. 27 MHz and at a temperature of 310 K using a Bruker AVANCE-II 600 NMR spectrometer operated by TOPSPIN 3. 1 software (both from Bruker Biospin). For qualitative and quantitative data analysis, we used AMIX (Bruker Biospin, version 3. 9. 14). We used the AMIX Underground Removal Tool on obtained NMR-spectra to correct the baseline, thereby using the following parameters: left border region 20 ppm and right border region -20 ppm and a filter width of 10 Hz.
Absolute quantification was performed as previously described  (58). In brief, a signal of the metabolite, either a complete signal or a proportion, was chosen manually and integrated. The area was further normalized on the area of the internal standard TSP and on the corresponding number of protons and the sample volume. For statistical comparison of extracellular metabolite data and growth, barcharts, and XY-plots, we used Prism (version 6. 01; GraphPad Software). The time-resolved extracellular metabolite concentrations were log2 (x + 1) transformed for the separation via PCA. The PCA was done using PAST v3. 16 with auto-scaled data  (59).
After quality control and trimming the sequence reads were mapped onto the genome (genome-build-accession NCBI Assembly: GCA_000009045. 1) using the Torrent Mapping Alignment Program. The Ion Proton system generates sequence reads of variable lengths, and this program combines a short read algorithm  (61) , and long read algorithms  (62)  in a multistage mapping approach. The gene expression levels were quantified using HTseq  (63). The data was normalized and analysed for differential expression using R statistical software and the DESeq2 package  (64). The RNA-seq data have been submitted to and are accessible in the Gene Expression Omnibus (GEO) using accession number GSE121479.
ACKNOWLEDGEMENTS We would like to thank  Henrik Strahl  ( Newcastle University ) for scientific assistance with the lipid analysis and for insightful discussions,  Johan Westerhuis  ( University of Amsterdam ) for his insights about the analysis of the exometabolome,  Selina van Leeuwen  ( MAD, UvA ) for providing excellent sequencing services, and to all the members of the  Bacterial Cell Biology group (University of Amsterdam) , and specially to all the members of the  Advanced Multidisciplinary Training in Molecular Bacteriology (AMBER) EU Marie Curie Initial Training Network (ITN). The research was funded by  EU Marie Curie ITN  grant  AMBER  ( 317338 ),  Marie Curie CIG  grant  DIVANTI  ( 618452 ),  European Commission   MCSA-IF 749510 , and  STW Vici  grant  12128.
Recently, the screening of a large set of predicted carbohydrate active enzymes (e. g. , CAZymes) revealed new enzyme activities and led to the creation of new CAZy families  [10]. Among the screened enzymes, we found that the Bacteroides cellulosilyticus WH2 hyaluronan lyase (Genbank ALJ61728. 1) grouped in the newly created PL33 family, was active in diabolican, providing a new series of oligosaccharides. Herein, we report the full NMR analyses of the four oligosaccharide end-products.
NMR Carbon-13 and proton NMR spectra were recorded with a Bruker Avance 400 spectrometer operating at a frequency of 100. 618 MHz for 13 C and 400. 13 MHz for 1 H. Samples were solubilized in D2O (pH = 6. 5) and analyzed at a temperature of 293 K. Residual signal of the solvent was used as internal standard: HOD at 4. 85 ppm at 293 K. 13 C spectra were recorded using 90° pulses, 20,000 Hz spectral width, 65,536 data points, 1. 638 s acquisition The possible cleavage site of the polysaccharide (Figure  5 ) explained the production of the heptasaccharide, which could be further degraded in tri-and tetrasaccharide. The complete attribution of the protons and carbons of all the residues as well as the linkages between the residues resembling what was reported for the tri-and tetrasaccharides confirmed the heptasaccharide structure: ∆4,5UA-β-1,4-GalNAc-α-1,3-GlcNAc-β-1,4-GlcA-β-1,4-GlcA-β-1,4-GalNAc-α-1,3-GlcNAcrα/β.
NMR Carbon-13 and proton NMR spectra were recorded with a Bruker Avance 400 spectrometer operating at a frequency of 100. 618 MHz for 13 C and 400. 13 MHz for 1 H. Samples were solubilized in D 2 O (pH = 6. 5) and analyzed at a temperature of 293 K. Residual signal of the solvent was used as internal standard: HOD at 4. 85 ppm at 293 K. 13 C spectra were recorded using 90 • pulses, 20,000 Hz spectral width, 65,536 data points, 1. 638 s acquisition time, 1 s relaxation delay and between 8192 and 16,834 scans. Proton spectra were recorded with a 4006 Hz spectral width, 32,768 data points, 4. 089 s acquisition times, 0. 1 s relaxation delays and 16 scans. The 1H and 13 C-NMR assignments were based on 1 H-1 H homonuclear and 1 H-13 C heteronuclear correlation experiments (correlation spectroscopy, COSY; heteronuclear multiple-bond correlation, HMBC; heteronuclear single quantum correlation, HSQC).
Funding: This work was supported by the  French National Research Agency  (Grant  ANR-14-CE06-0017 ). W. H. has received support from the  Glyco@Alps Cross-Disciplinary Program  (Grant  ANR-15-IDEX-02 ),  Labex ARCANE , and  Grenoble Graduate School in Chemistry, Biology, and Health  (Grant  ANR-17-EURE-0003 ).
False -true positive rate of structural de novo mutation detection The total number of de novo structural mutations candidates was 26 considering the 5 species and the 3 software (Table  1 ), but PCR and Sanger sequencing check considerably reduced this number to 1, given a very low true positive rate of 4%. From Sanger sequencing, the 8 tested breaks from svaba and delly were false, so we considered all other non-tested breaks (10) as false. The other variants were deletions (5) and duplications (3), all larger than 300 nt apart one deletion of 215 pb. According to Wala et al  (Wala et al. , 2018) , the positive rates of deletions larger than 300 bp are 85% for svaba and delly, and 95% for lumpy; but for insertion/duplication, the validation rates decrease to 37%, 24% and 29%. Here, the positive rate of deletion is 20%, substantially lower than previously reported.
Astrocytes in the superior colliculus have a large territory and complex morphology To assess region-specific morphological characteristics of astrocytes, we analysed the domain area and complexity of GFP-positive astrocytes from superficial SC (n = 8), V1 (n = 11) and hippocampal CA1 (n = 11) of GFAP-eGFP mice. Reconstruction of single astrocytes revealed morphological differences between brain regions (Figure  2a, b ). We found no significant differences in soma size between the three brain structures (Figure  2c , p = 0. 5940, one-way ANOVA). However, astrocytes in the SC cover a larger territory compared to V1 (Figure  2d , p = 0. 0227 between SC (n = 9) and V1 (n = 11), Tukey's post hoc test after one-way ANOVA). These data suggest that astroglial processes in the SC may be longer or more numerous to cover a larger area.
Briefly, this STED-microscope has 488nm, 561nm and 640nm confocal excitation lasers (Abberior Instruments, pulsed @40/80 Mhz) together with a STED-laser of 775nm (MPB-C,pulsed @40/80 Mhz) (Figure  4a ). For each brain region, one or two astrocytes per slice were imaged. For each astrocyte, 3 to 10 field of views with a z-stack of 0. 03µm intervals were acquired. Homer-1 and VGluT1 signals were acquired with STED and confocal resolutions simultaneously, while GFAP signal was only acquired with confocal resolution. These images were then deconvolved using the Huygens software and analysis was performed using the ImageJ software combined with an internally developed plugin to determine the distance from a detected excitatory synapse to the closest astrocytic process  40. In short, peak intensities of VGlut-1 and Homer-1 were identified in the STED images.
Acknowledgements The authors thank  Philippe Mailly  from the imaging platform for the analysis of STED images. Funding: This work was supported by the  European Research Council  (Consolidator grant # 683154 ) and  European Union 's  Horizon 2020 Research and Innovation Program  ( Marie Sklodowska-Curie Innovative Training Networks , grant # 722053 ,  EU-GliaPhD ) to N. R. and to C. H. , and by the  Fondation Recherche Médicale (FRM)  to J. V. Competing interests: The authors declare no competing interests.
The cycle was then paused to inject 20μl of the glucose solutions using a multichannel pipette, and the plate rapidly reloaded in the reader to restart measurements for another 17 cycles. Data were collected and analyzed in an Excel spreadsheet and in Prism 7. 0 (GraphPad). Emission intensity values of yeast transformed with an empty plasmid were subtracted from emissions values at 485 nm and 528 nm for each measurement at the different glucose concentrations. The emission intensity ratio for Citrine (528 nm) over CFP (485 nm) was calculated. All analyses were repeated (with three technical replicates) at least three times independently. Emission ratios were normalized to the average of the three initial ratio values before glucose addition.
Microscopy Cells were mounted in synthetic complete medium with the appropriate carbon source and observed with a motorized Olympus BX-61 fluorescence microscope equipped with an Olympus PlanApo 100× oil-immersion objective (1. 40 NA), a Spot 4. 05 charge-coupled device camera and the MetaVue acquisition software (Molecular Devices; Sunnyvale, CA) and imaged at room temperature. GFP-tagged proteins were visualized using a Chroma GFP II filter (excitation 440-470 nm). mCherry-tagged proteins were visualized using an HcRed I filter (excitation 525-575 nm). Images were processed in ImageJ (NIH). Vacuolar staining was obtained by incubating cells grown in the desired condition with 100 μM CMAC (Life Technologies) for 10 min under agitation at 30˚C. Cells are then washed twice with water before observations with a fluorescence microscope equipped with a DAPI filter.
Acknowledgments We thank  Agathe Verraes  for technical assistance,  Olivier Vincent  ( CSIC, Madrid, Spain ) and  Michel Becuwe  ( Harvard University, Cambridge, MA, USA ) for sharing reagents and  Alexandre Soulard  ( UniversiteClaude Bernard Lyon 1, Lyon, France ) for helpful discussions and critical reading of the manuscript. We also acknowledge the  IJM ImagoSeine facility , member of  IBiSA  and the  France-BioImaging  infrastructure (https://anr. fr/ProjetIA- 10-INBS-0004 ; ANR-10-INBS-04 ).
This work was supported by grants from  IDEx Universitede Paris  (https://u-paris. fr; ANR-18-IDEX-0001  to SL), the  Fondation ARC  pour la recherche sur le cancer (https://www. fondationarc. org, ARCPJA32020060002096  to SL), and individual fellowships from the Ligue contre le cancer (https://www. ligue-cancer. net; TAZK20115  to CL), the  Fondation pour la Recherche Medicale  (https://www. frm. org; SPF20150934065  to QD) and the  Ministère de l'Enseignement Superieur et de la Recherche  (https://www. enseignementsuprecherche. gouv. fr; to AB). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
, 2015). The DNA sample is then analyzed to answer the ambitious questions: "who is here?" and "what are they doing?" A variety of bioinformatic tools and software have been developed to annotate the sequences into taxonomic and functional categories. They can be grouped into two categories: (i) alignment-based methods that infer taxonomy/functions based on similarity of sequences along reference databases such as BLAST  (Altschul et al. , 1990)  and DIAMOND  (Buchfink et al. , 2014) , (ii) k-mer-based approaches such as kraken2  (Wood et al. , 2019)  and CENTRIFUGE  (Kim et al. , 2016). However, these technologies suffer from dependence on prior knowledge and are not able to annotate sequences absent from the databases (at least with no resemblance). A good criterion to compare results between software will be to evaluate the capacity to annotate the sequenced reads to a taxonomy/functional entity, but because the annotation depends on the technology used, the choice of the associated parameters, or the intrinsic factors of the studied environment (Figure  1 ), the comparison is not feasible without a unique benchmark.
Moreover, another aspect that affects the rate of annotation (e. g. , the capacity to annotate the sequenced reads) is the level of analysis, which might be in terms of taxonomy, rank (species to domain), functions, and gene/pathway. The more the annotation is specific (threshold of similarity and level of analysis), the more the rate of annotation will be low. Nevertheless, to obtain interpretable information, having a detailed annotation in terms of taxonomy and functions will help to interpret the generated data. The trade-off has to be set according to the studies and their scientific questions  (Inkpen et al. , 2017). In the last few years, new methods have emerged to analyze metagenomics data based on machine and deep learning approaches. These methods attempt to acquire the capacity to distinguish complex patterns among large datasets to make accurate predictions on future datasets that will be analyzed using the trained models  (Greener et al.
001-1% of the total bacterial diversity present on earth  (O'Leary et al. , 2016). This emphasizes that genomes in reference databases do not cover the total diversity in the metagenomic samples. Finally, the emergence of assembly and de novo metagenomic reconstruction of genomes from metagenomic data, also called metagenomic assembled genomes (MAGs), has unveiled the numerous uncultured microorganisms in multiple environments  (Qin et al. , 2010; Lee et al. , 2017; Delmont et al. , 2018; Kroeger et al. , 2018; Pedron et al. , 2019). Because the genomes are not yet cultured, they can represent multiple genomes, and their taxonomy affiliation cannot be connected to known species. They are generally named with an identifier, e. g. , (Genus) sp. (identifier) (for instance, Bacillus sp. M35), or are proposed with the species name preceded by the term "Candidatus.
" MAG permitted the acquisition of yet uncultured genomes, but integrating MAG into metagenomic classifiers is complex because they may not be regular genomes and they are not fully integrated into the taxonomy. Metagenomic data are also a source of functional information and, using reference databases, can be annotated to understand what the potential functions are that could reflect their ecological role in the studied environment. As there is a link between DNA sequences and functions, we can be more confident of the annotation process based on alignment, but the sequence similarity threshold to be confident is always a questionable point that will impact the annotation process  (Treiber et al. , 2020). Depending on the function, a different similarity percentage will be required to identify reads as a function.
Moreover, the choice of the database can change the level of analysis and the interpretation. In addition, there are no official functional categories and a variety of databases has emerged, each with its own specificities (Table  2 )  (Caspi et al. , 2014; Lombard et al. , 2014; Kanehisa et al. , 2016; Huerta-Cepas et al. , 2019; Gene Ontology Consortium [GOC] , 2021;  Mistry et al. , 2021; The UniProt Consortium et al. , 2021). These non-standardized annotations alter our capacity to compare tool accuracies.