dataesr/scientific-paragraphs-categorization huggingface dataset:
Unlike ESS-color, however, it does not use ESS, does not encode small class differences, and does not use local class tables. As expected, ESS-basic is not as effective as ESS-color, using up to 6. 9x more space than ESS-color (Table  2 ). These results are not surprising since ESS-basic does not exploit the redundancy created by shared k-mers across samples. For the assemblies datasets, the compression improvement of ESS-color over ESS-basic closely tracks that of ESS-color over the original fa. p7zip. For the sequencing datasets, ESS-basic uses between 1. 3 and 1. 9 more space than ESS-color. Tables  3  and 4  show the run time and memory usage of compression, respectively. Here, ESS-color is outperformed by other tools. In particular, if optimal compression space is not needed, then GGCAT becomes a good alternative to ESS-color.
Local tables are only beneficial when a single color class appears in more than one run in a simplitig, which apparently was rare. Second, the majority of simplitigs use maxDif = 0. This mode is optimal when the simplitig has just one color class. There is also a more complicated trade-off since setting maxDif > 0 adds one extra bit for each run encoding, which may outweigh the benefits of encoding some k-mers with a class difference. Third, the Gut dataset demonstrates the benefit of encoding class differences, especially compared to GGCAT. It is the dataset with the highest percentage of simplitigs using maxDif > 0 (21%) and, simultaneously, it is also the dataset where GGCAT uses the most space relative to ESS-color (44%).
Conclusion Colored de Bruijn graphs are a popular way to represent sequence databases. In spite of their ever-growing sizes, there have not been many specialized tools for compressing them to disk. In this paper, we present a novel disk compression algorithm tailormade for cdBGs that achieves superior space compression compared to all other tools on the evaluated datasets. Our algorithm is a novel combination of ideas borrowed from previous work on disk compression of k-mer sets and indexing of cdBGs. We use a spectrum-preserving string set (SPSS) as a basis for both compressing the nucleotide sequences and for ordering the rows in the color matrix. By using the SPSS ordering, we can avoid the costly storage of an indexed de Bruijn graph (e. g. BOSS in  [9, 14]  or a counting quotient filter in  [2] ).
Funding This material is based upon work supported by the  National Science Foundation  under grant nos. 2138585  and  1931531. Research reported in this publication was also supported by the  National Institutes of Health  under Grant  NIH   R01GM146462  (to P. M. ). A. R. was supported by the  National Institutes of Health Computation, Bioinformatics, and Statistics (CBIOS)  training program. The content is solely the responsibility of the authors and does not necessarily represent the official views of the  National Institutes of Health. Acknowledgements We would like to thank  R. Chikhi  for helpful discussions.
It may also support the generation of a new understanding on the function of such complex biological systems that microbial biofilms represent. However, due to gaps in current molecular databases, metabolite annotation levels remain still very limited  (da Silva et al. , 2015)  and concern around 5% of the whole metabolomic fingerprint signal  (Dias et al. , 2016). However, a recent study proposed to develop an innovative approach using the trends observed in dose-response curves of meta-metabolomic features and the determination of the benchmark dose-responses (BMDs) to overcome this pitfall  (Colas et al. , 2023). As such, that approach takes the full advantage of meta-metabolomic investigations for ecotoxicology purposes. In this work, river biofilms were exposed to Co for 7 days and untargeted metametabolomic features were analyzed at each tested concentration.
The V8-V9 region of the 18S rRNA encoding gene was amplified using primers V8F (5'-ATAACAGGTCTGTGATGCCCT-3') and 1510R (5'-CCTTCYGCAGGTTCACCTAC-3')  (Bradley et al. , 2016). The amplicons were sequenced on an Illumina NextSeq 2000 300x2 bp platform (PGTB platform, Cestas, France; doi: 10. 15454/1. 5572396583599417E12). Sequence analyses were performed on the Galaxy FROGS pipeline  (Afgan et al. , 2018). Forward and reverse reads were timed at 280 and 275 bp, respectively. Amplicon Sequence Variants (ASVs) were obtained with the default parameters and affiliated with the 16S SILVA 138. 1 AND 18S SILVA 138. 1 databases for prokaryote and eukaryote community characterization, respectively. Alpha-and betadiversity metrics were computed in RStudio (v4. 1. 2)(R Core Team, 2021) with the R package phyloseq (v1.
Multivariate Omics Trajectory Analysis (MOTA) The dynamics of biofilm meta-metabolome changes versus compositions of prokaryotic and eukaryotic biofilm communities were investigated by comparing trajectories of centroids using the Multivariate Omics Trajectory Analysis method (MOTA)  (Foucault et al. , 2022). Datasets of meta-metabolome, prokaryotic and eukaryotic biofilm communities were log2 transformed and analyzed identically. Median distances between Principal Component Analyses' (PCA) centroid coordinates were computed by median to create a trajectory between each sampling time point for the four exposure conditions and displayed as the fraction of the total length achieved at each time point (from 0 to 100% between hour 1 to day 28). Trajectories were plotted for the exposure time (log10 transformed) versus meta-metabolomic data, 16S and 18S rRNA data for each exposure concentration, allowing comparison of their dynamics over time.
Untargeted meta-metabolomic features In total, 5,141 untargeted meta-metabolomic features were observed among all samples while only 192 (3. 7%) annotations were attempted using the molecular network approach supported by public spectral database inquiry. However, over 92% of the global metametabolome appeared significantly impacted during the 28 d of exposure. Cobalt concentrations had a significant effect on the contents of 2,356 untargeted meta-metabolomic features, exposure time on the contents of 4,703 untargeted meta-metabolomic features and their interaction induced a change in the contents of 1,950 untargeted meta-metabolomic features (two-way ANOVA, p <0. 05). A gradual change over time in the meta-metabolomic fingerprints of biofilms with the influence of Co exposure concentrations was observed (Fig.
Acknowledgments: We would like to thank the members of the  Environment & Sustainable Development Team, at Pôle d'Etudes et de Recherche de Lacq (PERL)  for access to the TotalEnergies facilities for mesocosm exposures and assistance during experimentation. We are grateful to the  UMR 7245 MCAM ,  Muséum National d'Histoire Naturelle, Paris, France , especially the "Cyanobactéries,  Cyanotoxines et Environnement" (CCE)  team and the  Plateau technique de spectrométrie de masse bio-organique (PtSMB)  for laboratories and technical facilities for meta-metabolomics analysis. Part of the experiments (amplicon sequencing) were performed at the PGTB (doi:10. 15454/1. 5572396583599417E12) with the assistance of  Zachary Allouche  and  Zoé Delporte. We also thank  Claire Gassie  ( Universite de Pau et des Pays de l'Adour, E2S-UPPA, CNRS, IPREM, Pau, France ) for her help in characterization of prokaryotic and eukaryotic communities.
The ferric iron state structure determined by serial femtosecond crystallography (SFX). The SFX experiment used short (10 fs), high-intensity X-ray pulses from the SACLA XFEL to provide diffraction patterns that are collected before the crystal is destroyed  (35). It has been shown that data can be recorded free of the effects of radiation induced changes as long as sufficiently short pulses (<20 fs) are used  (36). Crystallization conditions were optimized to obtain microcrystal slurries suitable for SFX, as described by us previously  (37). For data collection, crystals of approximately 20 x 7 x 7 µm 3 were applied onto a fixed-target silicon chip, with the final dataset merged from three chips (Table  S1 ). SFX and neutron diffraction structures are similar (see comparison in SI), with the Arg103 side chain contributing to the second shell, but the side chain of Arg203 pointing away from the binding site, Fig.
3C , while the SFX XRPP approach preserved the electron density, Fig. 4. This may be attributed to the different time domains probed, or the increasing non-isomorphousness in the SSX dose series  (43, 44). The dose delivered in a 10-femtosecond pulse in the SFX XRPP pump was with 500 kGy about 4. 5-5. 6 times higher than the doses used in the home source or SSX experiments, respectively. However, the home source dataset required data collection for 1 hour, while the SSX dose series was collected in 110 ms per crystal, and the SFX XRPP experiment was collected in 33 ms per crystal. Further to the different timescales, the SFX experiment used pulsed rather than continuous illumination, and/or could differ in heat load. Beam sizes were also different, with 10 micron for SSX and 1. 5 micron for SFX experiments, providing an important difference for photoelectron escape  (45).
The X-ray beam with a flux of 2. 5 x 10 9 ph/s at 8. 1 keV was collimated at 200 µm 2. The total exposure time of 1 hr equated to a total dose of 110 kGy. Neutron crystallography. For hydrogen-deuterium exchange, FutA crystals grown from batch crystallization were transferred into crystal mother liquor prepared with deuterated water, in two subsequent exchanges for 24 hrs. Crystals with a volume larger than 0. 2 mm 3 were mounted in 1 mm sealed quartz capillaries. Data collection at BIODIFF  (48) , Forschungsreaktor München II (Germany) used a monochromatic neutron beam. The final dataset was merged from two isomorphous crystals collected at wavelengths of 3. 1023 and 3. 0946 Å.
Serial synchrotron crystallography (SSX). SSX data were collected at beamline I24, Diamond Light Source, using silicon chips with 12 µm apertures. At each aperture ten images were collected to obtain two series with 5 kGy and 22 kGy dose-slicing (10 ms per image). Images were separated into individual dose points for processing  (41). Datasets above a total dose of 110 kGy were no longer isomorphous with the lowest dose point, likely due to global damage, as corroborated by the increase in B-factors, and tying with observation of difference density features "waning" at higher doses. Serial femtosecond crystallography (SFX). SFX data were collected at SACLA beamline BL2 EH3, Japan, using the MPCCD detector. The XFEL was operated at an X-ray energy of 11. 0 keV with a pulse length of 10 fs and a repetition rate of 30 Hz.
The iron binding site in FpbA from T. thermophilus differs from the other proteins, as the residues equivalent to Prochlorococcus MED4 FutA His12 and Tyr13 are exchanged to Glycine and Glutamine, respectively. Despite this, the residue equivalent to Arg203 is conserved (Arg223). As FbpA switches from an open (PDB: 3WAE) to a closed conformation (PDB: 4ELR) upon iron binding, a carbonate ion is lost from the binding site and Arg223 is repositioned away from the binding site, Fig. S4  (24, 25). This ability of Arg223 to act as a structural switch and maintain a net neutral charge in the binding site is therefore highly similar to the observed structural rearrangement of Arg203 in Prochlorococcus MED4 FutA.
Acknowledgments We thank  Chris Holes  for macromolecular crystallization,  Peter Horton  for diffraction, and  Peter Roach  for critical discussion at the  University of Southampton (UoS). We are grateful to SACLA beamline staff, in particular  Shigeki Owada  and  Kensuke Tono  for technical assistance. We acknowledge financial support:  Biological Sciences Research Council (BBSRC)  to the  XFEL program  under  Japan Partnering Award   BB/R021015/1  and  BB/W001950/1 ; the  Japan Synchrotron Radiation Research Institute   2018A8005  and  2019A8004 ; the  UK XFEL Hub ,  Diamond Light Source (DLS)  for travel support; studentship support by the  South Coast Biosciences Doctoral Training Partnership  ( SoCoBio  DTP) to JS, the  National Oceanography Center  to MoM, the DLS and UoS to RB, MoM, JS, MR, and  Hamburg University  to NC.
UV-vis absorption spectroscopy experiments were carried out using the BM07-FIP2 and icOS platforms of the Grenoble Instruct-ERIC Center (ISBG; 404 UAR 3518  CNRS-CEA-UGA-EMBL)  within the  Grenoble Partnership for Structural Biology  supported by  FRISBI  ( ANR-10-INBS-0005-02 ) and the  University Grenoble Alpes  graduate school supported by  CBH-EUR-GS  ( ANR-17-EURE-0003 ), carried out in the RADDAM bag. AMO and JS were supported in part by  DLS , the  UK Science and Technology Facilities Council , the  BBSRC , the  Wellcome Investigator  Award  210734/Z/18/Z , and a  Royal Society Wolfson Fellowship   RSWF\R2\182017  (all to AMO).
6 (90. 6) 92. 3 (43. 1) 97. 0 (40. 0) I σI ⁄ 1 71. 7 (11. 6) 4. 8 (2. 0) 12. 1 (4. 6) 3. 14 (0. 41) 3. 67 (0. 43) Completeness (%) 1 97. 9 (83. 5) 81. 0 (52. 6) 99. 8 (100. 0) 100. 0 (100. 0) 100. 0 (100. 0) Multiplicity 1 65. 4 (42. 1) 1. 9 (1. 1) 618. 7 (274. 5) 26. 2 (19. 1) 165. 2 (82. 4) Unique Reflections 1 30,881 (1,369) 13,731 (890) 37,266 29,256 (1,467) 34,653 (1676) (1,877) Wilson B-factor (Å 2 ) 14. 10 10. 07 12. 09 24. 28 20. 98 Refinement statistics Home Source Neutron SFX SSX 5 kGy SFX 500 kGy yellow blue green purple green PDB Code 8OEM 8OEN 8C4Y 8OGG 8OEI Resolution (Å) 47. 50 -1. 70 24. 95 -2. 1 30. 10 -1. 60 40. 97 -1. 76 32. 42 -1. 65 Rwork/Rfree 0. 153 / 0. 180 0. 182 / 0. 250 0. 190 / 0. 208 0. 202 / 0. 241 0. 162 / 0. 189 # Reflections all/free 30847 / 1,588 13,731 / 698 37,266 /1919 29256 / 1503 34653 / 1786 Number of Atoms Protein 2558 4984 2485 2509 2572 Ion 1 1 1 1 1 Water 117 81 86 81 105 Ramachandran Preferred 305 298 304 301 302 Allowed 4 11 5 8 7 Outliers 1 1 1 1 1 B-factors (Å 2 ) Protein 19.
The peptide crosslinks to one uridine (mass shift 324) and could be identi ed by "light" searches (based on the expected mass of the RNA-peptide adduct) and by "lightheavy" searches (considering the characteristic peak doublet resulting from the isotope labelling), ensuring the speci city for the differentially isotope labelled RNA (Fig. 2e ). According to a crystal structure (PDB: 6FLN  35  ) this region is devoid of secondary structure (Fig. 2b ). Comparative analysis of TRIM25 sequences from different species (see Supplementary Table  2 ) revealed that the speci c region responsible for RNA binding, as identi ed by CLIR-MS, is well conserved (Extended Data Fig. 2e ). However, this region is not conserved in other group IV TRIM protein CC domains  9  (Extended Data Fig. 2f ). It is noteworthy that this region overlaps with the surface that interacts with the PRY/SPRY domain identi ed by Koliopoulos et al.
Our iCLIP2 data allowed us to describe a structural preference of TRIM25 for stem-loop structures inside the cell, including both host and viral RNA. This observation can also explain the G-and C-rich sequences observed in the previous dataset, as G-C base pairing is more stable than A-U due to the higher number of hydrogen bonds formed. We also identi ed a preferred sequence motif within the hairpin loop of these stem-loop structures. Particularly, we found an AGAA motif that precedes the crosslinking site followed by a UGG motif. Interestingly, the sequence motif found is present in previously identi ed RNAs that bind TRIM25, such as pre-let-7 RNA and DENV-SL. Furthermore, this motif differs from the one present in the Lnczc3h7a RNA, which is consistent with the weaker binding we observed here.
(b) The surface potential representation of the CC dimer (PDB:4LTB) indicates a positively charged surface. The enlargement of this interface shows potential RNA binding residues. Mutation of residues K283 and K285, shown in grey, leads to a TRIM25-CC mutant we term m2. In purple, the peptide detected by CLIR-MS/MS (see below) is highlighted including the four potential RNA binding residues. Mutation of these four residues leads to a TRIM25-CC mutant we term m4. (c) Representative ITC binding isotherm for TRIM25 CC-m2 with pre-let-7 complex (n=3) (Supplementary Table  1 ) (d) Schematic representation of the CLIR-MS/MS method. The RNA binding protein (grey) is crosslinked to an equimolar mixture of unlabelled (black) and uniformly  13  C/ 15 N-labelled RNA (red). (e) MS spectrum of the TRIM25-derived peptide GISTKPVYIPEVELNHK crosslinked to a single U nucleotide (see methods).
ACKNOWLEDGEMENTS L. A. was supported by a fellowship from the  EMBL Interdisciplinary ( EI4POD) program  under  Marie Skłodowska-Curie Actions COFUND  ( 847543 ) and acknowledges support by the  Joachim Herz Foundation  through an Add -on Fellowship for Interdisciplinary Life  Sciences. L. I. is funded by  BBSRC   DTP scholarship  number  BB/M011224/1 V. R. is funded by the  European Union 's  Horizon 2020 research and innovation programme  under  Marie-Sklodowska-Curie  n. 892756. K. R. is supported by the  Francis Crick Institute , which receives its core funding from  Cancer Research UK  ( CC2075 ), the  UK Medical Research Council  ( CC2075 ), and the  Wellcome Trust  ( CC2075 ). A. L. acknowledges  Paola Picotti  ( ETH Zurich ) for access to instrumentation and laboratory infrastructure.
Choice of animal model and cortical state The three animal models most utilized in visual neuroscience are cat, Macaque monkey and, recently, mouse. The most extensive body of data on primary visual cortex has been collected for more than half a century in cat. Several of the most important datasets for this study, such as extensive parametric studies of visual receptive fields, the extents of inter-areal connectivity in V1  (Stepanyants et al. 2008 ) and the relative strength of the different intra-areal pathways  (Binzegger et al. 2004) , have been collected in this species. Finally there is greater availability of in-vivo intracellular data, which are critical for constraining our model, from the anesthetized cat  (Monier et al. 2008; Fournier et al. 2014; Frégnac and Bathellier 2015; Gerard-Mercier et al.
This interspecies difference could in fact be more fundamental in nature than initially thought. Indeed, the group of Massimo Scanziani has recently shown in the rodent that one of the higher-order visual areas, which is involved in the perception of moving objects, does not depend on information from V1 at all  (Beltramo and Scanziani 2019). Instead, this region, known as the post-rhinal cortex (POR), appears to obtain visual data directly from an evolutionarily ancient sensory processing mediated by pulvinar and superior colliculus. On the other hand, it remains undisputed that advances in optogenetics are rapidly expanding our knowledge of mouse V1 and the semi-industrialization of data acquisition in this species generates datasets that are particularly suitable for the comprehensive data-driven approaches that we champion.
Software and implementation The model is implemented, and all experiments and analysis defined in the Mozaik framework  (Antolík and Davison 2013) , which is freely accessible at https://github. com/antolikjan/mozaik. The NEST simulator (Gewaltig and Diesmann 2007, version 2. 2. 1) was used as the simulation engine for all simulations described in this paper. Implicit in the 5 integrative program proposed here is that progress requires building on previous steps. For such a long-term coordinated effort across the field to be successful, an appropriate infrastructure of tools will be required to facilitate both the incremental construction and sharing of ever more complex models, and efficient, in-depth, systematic comparison of alternative explanations. To catalyze this process, we have built our model in a recent neural network modeling environment, Mozaik  (Antolík and Davison 2013) , optimized for efficient specification and reuse of model components, experimental and stimulation protocols and model analysis.
Acknowledgments This work was supported by the  Centre National de la Recherche Scientifique, Paris-Saclay IDEX (Neu-roSaclay) ; the  French National Research Agency  ( Complex-V1 ); the  European Union 's  Seventh Framework Program  under grant agreements  269921  (BrainScaleS) and  604102  ( Human Brain Project ) and through the project  Improvement of internationalization in the field of research and development  ( CZ. 02. 2. 69/0. 0/0. 0/17_050/0008466 ) at  Charles University.
Discovery of viruses associated with Bathyarchaeia In total, 367 metagenome-assembled genomes (MAGs) of Bathyarchaeia, including 68 high-quality MAGs (Extended Data Table  S1 ), were obtained from our previous results  8, 39  and public databases. Phylogenetic analysis based on a concatenated alignment of an optimized set of 51 marker proteins (see Methods) yielded 8 major clades of Bathyarchaeia that correspond to the orders in the latest taxonomy  40  (Fig. 1a  and Extended Data Fig. S1 ). We then searched for CRISPR-Cas systems in Bathyarchaeia MAGs and identified type I and type III CRISPR-cas loci in over one-fifth of the MAGs. Typically, a bathyarchaeal genome carries a single CRISPR-Cas system of, either type I (53 high quality MAGs) or type III (15 high quality MAGs). Notably, however, 11 MAGs were found to encompass both types of CRISPR-Cas systems (Fig.
1b ). To identify putative viruses of the Bathyarchaeia, a dataset of CRISPR spacers was compiled from all collected MAGs. After automatic screening followed by manual inspection (see Methods), 49 high-confidence CRISPR array from different Bathyarchaeia orders were identified, containing 1,602 spacers (Extended Data Fig. S2 ). The spacers were used to search for protospacers in the viral sequences of IMG/VR v3 database  41. Additionally, a search for potential proviruses in the host genomes was conducted (see Methods). In total, 56 contigs were assigned to Bathyarchaeia viruses based on CRISPR spacer matches or unequivocal integration into the host genome. Among these, 54 contigs were found to encode major capsid proteins (MCPs), attesting to their viral nature  42. Six viral contigs corresponded to complete genomes, as indicated by the presence of terminal repeats (Extended Data Table  S3 ).
Bathyarchaeia genome collection and classification. Bathyarchaeia MAGs (metagenome-assembled genomes) were obtained from our previous dataset 8,  39  and the NCBI (https://www. ncbi. nlm. nih. gov) and IMG/M (https://jgi. doe. gov) public database (up until June 30th, 2021). Bathyarchaeia genome classification was based on 51 highcoverage marker gene optimized from GTDB 71 v207 (TIGR01171 and TIGR02389 were excluded due to the low coverage). Marker genes were initially identified with GTDB-Tk v2. 1. 1 72 using 'identify' method. Only MAGs contain more than 50% of the marker genes were kept. Sequences were then aligned using MAFFT v7. 457  73  and trimmed with TrimAl v1. 4  74  (-gappyout). The maximum likelihood phylogenomic tree for concatenated 51 proteins was constructed with IQ-TREE 2.
0. 6 (best-fit model LG+F+R13, -B 1000, -alrt 1000). The quality, contamination, GC content, and other sequence information of Bathyarchaeia MAGs were assessed by checkM v1. 2. 2  75. Biome information was extracted from the GenBank file or relevant literatures. To delineate Bathyarchaeia orders, the maximum likelihood tree based on 51 proteins was converted to an ultra-metric tree and the relative evolutionary divergence (RED)  76  was calculated. An order was called when its branch length in the ultrametric tree corresponded to a RED value within the range of 0. 5 to 0. 7.