dataesr/scientific-paragraphs-categorization huggingface dataset:
In this sense, algorithmic disclosures would produce useful information, by dynamically adapting its content and format to what the recipient needs at the time she needs. Also, as articulated elsewhere, because co-regulated algorithmic disclosures would necessarily be targeted at  65  To make a parallel, this operation resembles the way Google search engine operates (through domains and supradomains). More specifically, when Google users are shown a picture and are asked to 'confirm' that what they see is a cat, they can confirm or not. If they do, they reinforce a node of the graph (that the picture shown is a cat and not, say, a muffin). Similarly, human stakeholders in the sandbox provide behavioral data that confirm a proposed clause or text, thus reinforcing nodes, and gradually strengthening the links in our knowledge graph.
3967, 11. 2. 2019. Note that exceptions to this light-handed approach have always existed: examples of ex ante regulation are the 'Access to account data rule' in the Fintech sector(Di Porto & Ghidini, 2020); the mandated exchange of electricity and gas smart metering information (Directives 2019/944/EC and 2009/73/EC); the access to electricity network data rule (Commission Regulation (EU) 2017/1485), and, of course, intelligent transport systems (Directive 2010/40/EU). 4 Executive summary of the Impact Assessment (SWD (2018) 139 final) accompanying the Proposal for Regulation Regulation 2019/1150, at 2. 5 See EU Regulation 2019/1150 of the European Parliament and the Council on 'Promoting fairness and transparency for business users of online intermediation services', of 20. 6. 2019, OJEU L-186 of 11.
GaitSmart measurement The GaitSmart system uses six inertial measurement units (IMU) to evaluate gait mechanics. These IMUs comprise three tri-axial accelerometers and three tri-axial gyroscopes, making it possible to measure movements in the sagittal and frontal plane  [13]. After synchronizing the IMUs using Poseidon software (Dynamic Metrics Limited, Codicote, UK) they were attached to the body. Two IMUs were placed on the pelvis, under the iliac crest, following the alignment of the pelvis. Then two other IMUs were placed on the widest part of the thighs, aligned in a straight vertical line. The last two IMUs were placed on the calves, on the belly of the gastrocnemius muscles  [11, 13]  (see Supplementary Fig. S1 , available at Rheumatology online). Subsequently, participants were asked to stand still for 5 s to calibrate the IMUs.
The participants were then asked to walk 15-20 m at their own self-selected speed and return. After performing the test, the IMUs were removed and attached to the laptop for analysis. The IMUs are accurate to 0. 11 , although the measurement error depends on positioning on the body. A previous study showed a reproducibility of 62. 8 and 63. 4 for knee ROM in swing and stance phase, respectively  [11, 14]. Poseidon software was used to extract and analyse data from the IMU sensors. The result is a report containing ROM of pelvis, hips, thighs, knees in swing and stance phase, and calves in the sagittal plane, stride duration, medial-lateral movement of thighs and calves, and symmetry scores between left and right. All parameters are presented in graphs and tables. Fifteen GaitSmart parameters were selected for statistical analysis based on previous research  [11, 14]  and clinical expertise; ROM for both knees in swing and stance, both hips and both calves were determined.
By adapting the gait pattern in an early OA stage, assisting the natural compensation strategy, it might be possible to slow down disease progression and postpone surgery. Therefore, first a prognostic value of GaitSmart parameters for disease progression should be established. These data become available within the APPROACH project when follow-up data are collected. When gait characteristics prove to be possibly modifiable prognostic factors, early detection of an unfavourable gait in combination with adequate adaptation strategies to this might become a feasible preventive strategy. Patients scheduled for total knee arthroplasty had a typical OA gait pattern (reduced knee ROM in stance and swing phase) before surgery. Fifty-two weeks post-operation, two-thirds of the patients still had OA gait characteristics, even though pain was reduced  [25].
Acknowledgements We thank  M. Lafeber-Melief  for performing the KIDA analysis on all radiographs. Author contributions: E. H. , D. H. , M. L. , M. K. , F. J. B. , I. H. and F. B. were responsible for acquisition of data. E. H. , D. H. , H. G. and P. W. were responsible for all analyses. All authors contributed to interpretation of data. E. H. , D. H. and P. W. were responsible for the first draft, all other authors critically revised the content. All authors approved the final version. Funding: This work was supported by the  Innovative Medicines Initiative Joint Undertaking  under Grant Agreement no. 115770 , resources of which are composed of financial contributions from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013 ) and EFPIA companies. See www. imi. europa. euand www.
Sanger sequencing was then performed on each band and confirmed the presence of the deletion (Supplementary Data 3). The same procedure was performed with Guarani cultivated with APMV and confirmed the presence of the wild-type genotype, but the mutant was not detected in this condition. Evolution of the mutant virophage and Tupanvirus during serial-passage experiments. Virus adaptation to new hosts combines two steps: first, the introduction of mutations allowing the virus to infect new hosts and then maintenance of these mutations during spread within the host population  25. With this in mind, we investigated the capacity of mutant Guarani to maintain the deletion during passage experiments using Tupanvirus Deep Ocean as a virus host. This Tupanvirus strain was selected because it enables the virophage to replicate with higher replication efficiency (Fig.
To investigate whether the Guarani obtained from Tupanvirus Deep Ocean and Tupanvirus Soda Lake were genetically different from that propagated with APMV, the virophage cultivated with each virus was submitted to genome sequencing with Illumina MiSeq. The genome of each virophage was assembled through Spades software (default parameters) and manual finishing, and analyzed as previously described by Mougari et al. 14, 53. Comparative genomic analysis and genome alignment were then conducted using Muscle software and BLASTn alignment (Basic Local Alignment Search Tool)  54. Standard PCR and Sanger sequencing. A specific PCR system targeting the collagen-like protein (ORF8) was designed and performed on the DNA extracted from supernatant containing the mutant genotype to confirm the occurrence of  deletion of the 81 nucleotide-long sequence in strain (Fig.
2a ). The details of the primers used are listed in Table  1 , and the PCR product was visualized on an agarose 2% gel using SYBR safe buffer (Invitrogen, USA). The Qiagen gel extraction kit was used to recover the band corresponding to each genotype of Guarani detected in Tupanvirus supernatant (wild-type and mutant) from the 2% agarose gel according to the manufacturer's instructions. Sanger sequencing analyses were then performed in an Applied Biosystems ® 3130/3130xl Genetic Analyzer (Thermo Fisher, USA) using a Big Dye Terminator Cycle Sequencing Kit (Thermo Fisher, USA) according to the manufacturer's instructions. The obtained sequences were assembled with ChromasPro 1. 7. 7 software (Technelysium Pty Ltd, Australia). Passage experiments and inhibition assays. To investigate whether mutant Guarani was able to maintain the deletion mutation during several passages with Tupanvirus, A.
Acknowledgements This work was supported by a grant from the  French State  managed by the  National Research Agency  under the " Investissements d'avenir" (Investments for the Future) program  with the reference  ANR-10-IAHU-03  ( Méditerranée Infection ) and by  Région Provence-Alpes-Côte-d'Azur and European funding FEDER PRIMMI. Said Mougari  was supported by the  Foundation Méditerranée Infection scholarship. We are grateful for  Meriem Bekliz  for her help and assistance at the beginning of this project.
Data availability The genome of Guarani wild-type is available in the EMBL-EBI database under accession number LS999520. The collagen-like gene sequence of mutant Guarani has been deposited in the GenBank database under the accession number MT179725. The complete genome of mutant Guarani is available in Supplementary Data 2. The source data underlying plots shown in figures are provided in Supplementary Data 4. The authors declare that all other data supporting the findings of this study are available within the paper and its supplementary information files.
Small Molecule Screen to Identify Enhancers of Antigen Import into the Cytosol (A) Schematic representation of the b-lactamase assay used to monitor the efficiency of antigen import into the cytosol. MutuDCs were fed with b-lactamase for 3 h followed by 2 h incubation with small molecules (at 37 C). CCF4 loading was performed at room temperature for 1 h, and followed by 16 h incubation at RT to increase the sensitivity of the assay  (Zlokarnik et al. , 1998). Change in CCF4 fluorescence was monitored by flow cytometry. (B) Differential effects of ERAD inhibitors on antigen import into the cytosol. Representative flow cytometry data for selected ERAD inhibitors and quantification of the fold change in antigen import relative to DMSO controls. IMP, importazole; EerI, Eeyarestatin I. Means ± SE (dots represents data from independent experiments).
EC 50 values (concentration required for 50% of maximal activity) values were calculates as described in Figure  S2. Information about chemical classes and candidate targets was obtained from the DrugBank database. The classes and targets enriched in the group of active versus non-active compounds are represented with colored squares. The enrichment of targets for hits (compared to the entire library) was calculated using Fisher's exact test. (G) Analysis of gene and protein expression in CD8 + cDC1s. mRNA expression data (RNA sequencing [RNA-seq]) for CD8 + splenic DCs was downloaded from the www. Immgen. org database (GEO: GSE109125), and whole cell proteomic abundance data were generated by mass spectrometry from MutuDCs. 7427 proteins were detected by proteomics (blue dots) and selected markers highly expressed in cDC1s are highlighted with large blue circles.
Absolute copy numbers for all proteins detected in whole cell MutuDC proteome are available via the web resource (http://dc-biology. mrc-lmb. cam. ac. uk). The lower panel (gray dots) includes proteins not detected by proteomics. Targets enriched in the group of active versus non-active compounds are highlighted (Esr1, Drd2, and Slc6a4), as well as targets of the three active quinazolinamines (Adra1b for prazosin, Adra1a for doxazosin, and Egfr for gefitinib). See also Figures  S1  and S2  and Data S1  and S2. Cell Reports 32, 107905, July 14, 2020 3  (Figures 1B and S1). This effect was not recapitulated with a more potent p97 inhibitor, NMS-873, suggesting it might be due to offtarget activity. Hence, although these data highlight the role of the ubiquitin system in antigen import, they did not provide evidence supporting the role of other ERAD components.
Resource b-Lactamase-Based Screen for Enhancers of Antigen Import Enhancement of antigen import by DbeQ and importazole established a proof of concept that this process can be pharmacologically manipulated and prompted us to develop a screen for small molecule import enhancers. We performed the screen in Mu-tuDCs using the b-lactamase-based antigen import assay and a library of 786 FDA-approved drugs (Data S1). DbeQ and PR-619 were used as controls on each plate to track data quality (Figure  1C ). We selected 37 drugs that increased antigen import at least 2-fold in the primary screen for follow up (Figures  1D  and 1E ). Two non-active compounds were also included as additional negative controls (fosfomycin and thioguanine). 32 out of the 37 compounds exhibited a dose-dependent effect in the secondary screen (86% validation rate, 4% hit rate) (Figures  1F  and S2 ).
They included three classes of chemically related compounds: quinazolinamines (prazosin, doxazosin, and gefitinib), stilbenes (clomiphene, raloxifene, tamoxifen, and toremifene), and phenothiazines (chlorpromazine, fluphenazine, perphenazine, thioridazine, and trifluoperazine) (Figures  1F  and S1 ). To understand the mechanism of antigen import enhancement, we first searched for common targets among the active compounds. Using the DrugBank database  (Law et al. , 2014) , we identified previously described targets for 714 of the compounds present in the FDA library. Three of these targets were significantly enriched among active versus non-active compounds: estrogen receptor (Esr1), dopaminergic receptor (Drd2), and serotonin transporter (Slc6a4) (Figure  1F , lower panel). However, none of these three proteins is actually expressed in CD8 + cDC1s according to Immunological Genome Project expression data  (Yoshida et al.
Prazosin and Tamoxifen Induce Lysosome Permeability To determine whether lysosomal contents in prazosin-and tamoxifen-treated cells are secreted or leaked, we performed quantitative proteomic analyses of whole cell extracts and cytosolic fractions (Data S3). We observed significantly elevated levels of lysosomal enzymes in the cytosol of prazosin and tamoxifen-treated MutuDCs relative to control cells (Figure  4A ). Because the levels of these proteins were not changed in whole cell proteome (Figures  4A  and S4 ), we concluded that both prazosin and tamoxifen facilitate lysosomal leakage. Similar to what we observed using organellar maps, the prazosin effect is mostly restricted to lysosomal proteins, whereas tamoxifen affects a larger and more diverse set of proteins. To rule out that the observed lysosomal leakage was caused by increased compartment fragility and enhanced rupture during cell fractionation, we tested permeability of endolysosomal Cell Reports 32, 107905, July 14, 2020 5 Resource compartments in live cells.
Proportion of the live cells with a high ratio of blue to green (V450/V530) fluorescence was used as a measure of the efficiency of antigen import into the cytosol. Primary screen of the FDA library. Enzo FDA-approved drug library was screened in the course of three independent experiments. Each 96-well plate contained three media-only, DMSO only, 4 mM DbeQ (enhancement control), and 10 mM PR-619 (inhibition control) wells to control for data reproducibility between the plates. The screen was performed once and 37 top ranked compounds were selected for validation. Validation screen. The secondary screen was performed at six concentrations (1. 25 -40 mM) for each compound, in two biological repeats. Media-only and vehicle (DMSO) controls were included on each plate. Wells with less than 500 cells were excluded from analysis.
Chemical class and target assignment The information about chemical classes and candidate targets was downloaded from DrugBank database  (Law et al. , 2014). The enrichment of chemical classes and targets in active versus non-active compound groups was calculated using Fisher's test (R studio). Only the primary target was selected for each drug for the enrichment analysis. Cross-presentation assay 1x10 5 MutuDC were seeded in round bottom 96-well plates and incubated with different concentrations of soluble grade VII OVA (Sigma Aldrich Cat#A7641) or endotoxin-free OVA (Hyglos Cat#300036, Invivogen Cat#vac-pova). Minimal peptide OVA257-264 was used as a control for the capacity of DCs to activate T cells. As indicated, MutuDCs were either incubated with OVA for 45 min, followed by a 3. 5 h incubation with small molecules or incubated with OVA and small molecules continuously for 5 h.
Mass spectrometry and data processing Mass spectrometric analysis was performed as described  (Itzhak et al. , 2016) , using a Thermo EASY-nLC 1000 HPLC coupled to a Q Exactive HF Hybrid Quadrupole-Orbitrap (Thermo Fisher Scientific, Germany). HPLC gradient lengths varied for the different experiments. For analysis of whole proteomes, each of the SCX peptide fractions was analyzed with a 240 min gradient (24 h per sample in total). For the analysis of cytosol and fractions from the organellar maps, each sample was analyzed with a single 150 min gradient. Raw files were processed with MaxQuant software Version 1. 6  (Cox and Mann, 2008) , using the murine reference proteome (Swiss-Prot canonical and isoform data) database downloaded from UniProt (https://www. uniprot. org:443/).
Copy number estimates of proteins expressed in MutuDC To estimate absolute protein abundance in MutuDCs, the SILAC datasets used for full proteome analysis of drug-treated cells were used (see below). Each of the four dataset combined control cells and drug treated cells. From each dataset, the protein intensities from the control cells were selected, to obtain four replicate full proteomes. Intensities within each replicate were summed, and all replicates were linearly normalized to the same summed intensity. Next, only proteins detected in at least two replicates were retained (7427 in total). Copy number estimates were calculated using the Proteomic Ruler  (Wi sniewski et al. , 2014) , as implemented in Perseus software (V1. 5)  (Tyanova et al. , 2016) , and described in  Itzhak et al. (2019).
Drug-induced changes in whole cell proteomes For analysis of drug-induced changes in whole cell proteomes, only proteins with at least three SILAC quantification events in each of the four experiments (2 x control versus tamoxifen, 2 x control versus prazosin) were retained (5848 proteins). SILAC ratios were linearly normalized to a column median of 1 in each experiment, logarithmised, and analyzed with the 'Significance A' tool in Perseus software  (Tyanova et al. , 2016). Proteins that changed significantly in both replicate experiments with one drug (FDR = 0. 05 within each replicate, Benjamini-Hochberg correction), with a consistent direction of change, were considered as hits for this drug. Proteins that changed significantly across both replicates and both drug treatments, with a consistent direction of change in all four measurements, were considered as hits common to both drugs.
Subcellular localization predictions in MutuDC Organellar maps from the two control map replicates (0-1 normalized, Data S2) were annotated with 559 markers for 12 organellar compartments, by cross-matching our previously derived set of human marker proteins  (Itzhak et al. , 2016). Support vector machines (implemented in Perseus software, V1. 6)  (Tyanova et al. , 2016)  were trained to predict organellar association as described  (Itzhak et al. , 2016) , with an overall recall of 93% and a median F1 score of 0. 88 across all compartments (Data S2).
For every protein, four delta profiles, with five data points each (two sets from tamoxifen and two sets from prazosin treatment) were obtained. Delta profiles from treatment replicates were combined into one profile (ten data points) and analyzed with the multivariate outlier test in Perseus software (Perseus 1. 6, 101 iterations, quantile = n*0. 75) (Itzhak et al. , 2016). Movement (M) scores were calculated as the negative log(10) of the FDR corrected p values (Benjamini-Hochberg method). For example, an M score of four identifies significantly moving proteins with an FDR of 0. 01%. The reproducibility (R) score was calculated as the Pearson correlation of the two five-data point delta profiles within treatment replicates. A significance cut-off corresponding to a p value of 0. 05 (R = 0.
QUANTIFICATION AND STATISTICAL ANALYSIS Details of the statistical analysis are provided in Figure Legends and in STAR Methods. Plots were generated using GraphPad Prism version 8. 4. 2 for Mac (GraphPad Software, La Jolla California USA) or R (R Core Team, R: A Language and Environment for Statistical Computing (Version 3. 5. 0, R Foundation for Statistical Computing, Vienna, 2018; https://www. R-project. org/) and Tidyverse  (Wickham et al. , 2019).
ACKNOWLEDGMENTS We would like to thank  Hans Acha-Orbea  for the MutuDCs,  Matthew Albert  for the  NIH/3T3  cells expressing a non-secretable form of OVA,  Felix Randow  for the galectin-3-YFP construct,  Jon Howe  for help with microscopy, and  Greg S1odkowicz  for help with statistical analysis. D. N. I. and G. H. H. B. also wish to thank  Matthias Mann  for his support. S. A. received funding from  Institute Curie ;  Institut National de la Santeet de la Recherche Medicale ;  Centre National de la Recherche Scientifique ; la  Ligue Contre le Cancer (Equipe labellisee Ligue ,  EL2014. LNCC/SA );  Association de Recherche Contre le Cancer (ARC) ; the  ERC  ( 2013-AdG 340046  DCBIOX);  INCA  ( PLBIO13-057 );  ANR-11-LABX-0043  and  ANR-10-IDEX-0001-02   PSL ; and  ANR-16-CE15001801  and  ANR-16-CE18002003.
P. K. was supported by  EMBO  ( ALTF 467-2012 ), the  Wellcome Trust  ( 101578/Z/13/Z ), and  Medical Research Council  ( U105178805 ). A. A. was supported by  EMBO  ( ALTF 883-2011 ). D. N. I. was funded by the  Louis-Jeantet Foundation  and the  Max Planck Society for the Advancement of Science. G. H. H. B. was funded by the  German Research Foundation (DFG/Gottfried Wilhelm Leibniz Prize)  and the  Max Planck Society for the Advancement of Science.
Most of the phAPEC6 Genome Encodes for Unknown Proteins The phAPEC6 genome encodes 551 predicted open reading frames (Figure  2 ), which exceeds that of the smallest free-living bacterium, Mycoplasma genitalium (encoding 484 proteins)  [16]. PhAPEC6 can be considered as an isolate from the same species as coliphages SP27 (97% coverage, 99. 77% identity) (Azam et al. , 2019; unpublished; accession LC494302. 1), PBECO4  [17]  (95% coverage, 98. 26% identity) (accession KC295538. 1) and 121Q  [8, 13]  (96% coverage, 98. 94% identity) (accession KM507819. 1), belonging to the Asteriusvirus genus. A functional annotation of the genome (GenBank accession number MK817115) revealed that phAPEC6 encodes most DNA replication proteins, like two DNA polymerases, a clamp loader, a primase and three helicases, as well as a phage-encoded RNA polymerase sigma factor.
Phage Head Reconstruction Phage head images have been analyzed with the model-based PFT2/ EM3DR2 package  [45]. Particles (6915) have been selected by hand into 587 × 587 pixels 2 boxes using X3D  [46]  and corrected for the Contrast Transfer Function (CTF), as described previously (CTFMIX, 44). The 3D structure of phiRSL1  [21]  has been scaled to the same size as phAPEC6, low-pass-filtered to 45 Å and used as the starting model. Determination of particle origin and orientation were performed with the model-based PFT2 programs using an RSL1  [21]  low-resolution structure as the starting model. The finer angular step used for the refinement was 0. 2 degrees. Particles (4149) out of 6915 were used for the final reconstruction and the final resolution. The resolution of the final map was estimated to be around 10 Å by the Fourier shell correlation  [47]  calculated between independent half-dataset maps and applying a correlation limit of 0.
Funding: This work used the platforms of the Grenoble Instruct-ERIC centre (ISBG;  UMS 3518 CNRS-CEA-UGA-EMBL ) within the  Grenoble Partnership for Structural Biology  (PSB), supported by  FRISBI  ( ANR-10-INBS-05-02 ) and GRAL, financed within the  University Grenoble Alpes graduate school (Ecoles Universitaires de Recherche) CBH-EUR-GS  ( ANR-17-EURE-0003 ). The electron microscope facility is supported by the  Auvergne-Rhône-Alpes Region , the  Fondation Recherche Médicale (FRM) , the fonds  FEDER  and the  GIS-Infrastructures en Biologie Santé et Agronomie  (IBISA). IBS acknowledges integration into the  Interdisciplinary Research Institute of Grenoble (IRIG, CEA ). This study was also supported by the  AntibioPhage  Project, funded by an  Animal Health and Welfare ERA-Net (ANIHWA)  grant from the  EU   FP-Horizon 2020 Programme  and by grant  RT 09/8 APECON  from the  Federal Public Services of Health, Food Chain Safety and Environment (Belgium).