dataesr/scientific-paragraphs-categorization huggingface dataset:
Molecular replacement solutions were obtained with Phaser  (McCoy et al. , 2007)  (within Phenix) using the crystal structure of YTHDC1 [Protein Data Bank (PDB) code: 4r3i] as a template. The initial solution was then improved through cycles of manual adjustment in Coot  (Emsley and Cowtan, 2004) , automated building in phenix autobuild  (Terwilliger et al. , 2008)  and refined using Refmac5, phenix resolve or Buster (Global Phasing Ltd). Final pdb model corrections were performed using the pdbredo server  (Joosten et al. , 2014).
RNA-seq and sequence alignment RNA-sequencing was performed in duplicate for each experimental condition, following standard Illumina protocols, by GENEWIZ (South Plainfield, NJ, USA). Briefly, RNA quantity and integrity were determined using the Qubit Fluorometer and the Fragment Analyzer system with the PROSize 3. 0 software (Agilent Technologies, Palo Alto, California, USA). The RQN were ranging from 8. 8 to 10 for all samples, which was considered sufficient. Illumina TruSEQ RNA library prep and sequencing reagents were used following the manufacturer's recommendations using polyA-selected transcripts (Illumina, San Diego, CA, USA). The samples were sequenced on the Illumina NovaSeq platform (2 x 150 bp, single index) and generated ~20 million paired-end reads for each sample (Table  SX ).
The quality of the raw sequencing reads was assessed using FastQC (http://www. bioinformatics. babraham. ac. uk/projects/fastqc/) and MultiQC  (Ewels et al. , 2016). The RNA-Seq reads (FASTQ) were processed and analyzed using the Lasergene Genomics Suite version 15 (DNASTAR, Madison, WI, USA) using default parameters. The paired-end reads were uploaded onto the SeqMan NGen (version 15, DNASTAR. Madison, WI, USA) platform for reference-based assembly using the Toxoplasma Type II ME49 strain (ToxoDB-46, ME49 genome) as reference template. The ArrayStar module (version 15, DNASTAR. Madison, WI, USA) was used for normalization, and statistical analysis of uniquely mapped paired-end reads using the default parameters. The expression data quantification and normalization were calculated using the RPKM (Reads Per Kilobase of transcript per Million mapped reads) normalization method.
In parallel, the FASTQ reads were aligned to the ToxoDB-48 build of the Toxoplasma gondii ME49 genome (ToxoDB-48) using Subread version 2. 0. 1  (Liao et al. , 2013)  with the following options 'subread-align -d 50 -D 600 -sortReadsByCoordinates'. Read counts for each gene were calculated using featureCounts from the Subread package  (Liao et al. , 2014). Differential expression analysis was conducted using DESeq2 and default settings within the iDEP. 92 web interface  (Ge et al. , 2018). Transcripts were quantified and normalized using TPMCalculator (Vera  Alvarez et al. , 2019).
06. 5 and guppy v4. 09. Basecalling was performed during the run using the fast-basecalling algorithm with a Q score cutoff >7. Long read alignment (to ME49-Toxodb-13 and TAIR10 reference fasta files) was performed using Minimap2 (ver 2. 1) with the following parameters: '-ax splice -k14 -uf -G 5000 t 10 -secondary=no -sam-hit-only' for Toxoplasma and 'ax splice -k14 -uf -G 20000 t 10 -secondary=no -sam-hit-only' for Arabidopsis. Aligned reads were converted to bam, sorted, and indexed using Samtools. For T. gondii datasets, most sequencing runs were stopped after having generated between 400 k and 500 k of aligned reads to keep a standard of comparison (T. gondii reads varying between 30% and 70% of total mRNA depending on the preparation).
Chimeric transcript detection Chimeric reads covering two predicted transcripts were extracted using the ChimerID scripts  (Parker et al. , 2020)  (https://github. com/bartongroup/Simpson_Barton_Nanopore_1, Farhat, 2021a copy archived at swh:1:rev:1b509454a9e25a8c81be5092f8e525ca00e7b5a5) from native RNA Nanopore aligned reads within a Anaconda environment containing the following packages (snakemake ;pysam ; samtools ; scipy ; statsmodels ; py-bgzip ; tabix ; pytables ; bedtools ; minimap2 ; panda=0. 25. 3). Prior to running the analysis, the ToxoDB gene file (in gff format) was processed by AGAT (https://github. com/NBISweden/AGAT, Farhat, 2021b copy archived at swh:1: rev:692791aa30d253bd2ff83397d0dc1a74b5b52adb) to convert to GTF and modify the format. For practical reasons, all KEXXXX contigs were removed from the GTF and genome fasta file.
Once extracted, chimeric reads were counted against transcripts in the TGME49 gtf file using Htseq-count. This count file was also supplemented with normal counts of standard transcripts. Data was then normalized using a total aligned read normalization and ratios of Chimera over Total reads were calculated for every transcript in every condition. Log2Fold change in these ratios were then calculated to compare UT against IAA conditions for every KD condition (CPSF4, METTL3, and MORC). Transcripts with readthrough counts below three were excluded from further analysis. Of note, we had no mAID UT condition for MORC so we used the METTL3 UT condition as a comparison to calculate Log2Fch.
Differential error detection Differential error detection was performed using the Epinano pipeline 1. 2 (available code at, described in  Liu et al. , 2019). To limit unwanted background errors, DRS fast5 raw datafiles (of METTL3 UT and METTL3 24h-IAA or Arabidopsis WT and FIP37 ko) were re-basecalled on the Min-ionMK1C using the high accuracy basecalling option. Bam alignment files were generated with the following command "minimap2 -MD -ax splice -k14 -uf -t 12 ref-genome. fasta Guppy-HA-reads. fastq | samtools view -hbS -F 3844 -| samtools sort -@ 6 -o Alignement. bam'. Briefly, nucleotide variants were calculated on the aligned bam file for UT and KD/KO using the Epinano_Variants. py python script with normal parameters. A global error statistic by nucleotide position was calculated using the Epinano_sumErr.
py python script with a minimum of 50 reads per position. Finally, differential error z-scores were obtained by comparing the two datasets with the Rscript Epinano_DiffErr. R, the generated csv file was trimmed of unmodified residues using the linux « grep » command to select only the « mod » containing lines which have a z-score above 3. For representation, the generated data was converted to a bedgraph format representing nucleotide positions as a function of the epi-nano_DiffErr z-score * 10.
Acknowledgements The  HTX Lab (EMBL Grenoble ) are thanked for support in screening for crystal conditions and automatic mounting of crystals. We thank  Diamond Light Source  for beamtime and  David Aragao  for support. This work used the platforms of the Grenoble Instruct-ERIC center (ISBG;  UAR 3518 CNRS-CEA-UGA-EMBL ) within the  Grenoble Partnership for Structural Biology  (PSB), supported by  FRISBI  ( ANR-10-INBS-0005-02 ) and GRAL, financed within the  University Grenoble Alpes graduate school (Ecoles Universitaires de Recherche) CBH-EUR-GS  ( ANR-17-EURE-0003 ).
Supplementary Fig. S1c  shows the TEM image of the as-prepared Au 15 SG 13 NCs. The particles with sizes of 1-2 nm are barely discernible in the image. From the XPS data (Supplementary Table  S1 ), we find the Au/S atomic ratio to be 1. 26 ± 0. 13, which is compatible with the composition of Au 15 SG 13 (the expected value is 1. 15). Upon synthesis, the quality of Au 15 SG 13 was assessed using negative-mode ESI-MS. This analysis confirmed monodispersity of the synthesized NCs. A charge state distribution was observed from [M-4H]  4 -through [M-6H] 6-. Deconvolution of charge states from 4-through 6revealed a mass of 6928 Da for the Au 15 SG 13 , consistent with its calculated mass (Supplementary Fig. S2a ). To develop NCs with carbonyl-binding properties, Au 15 SG 13 had to be functionalized by the replacement of one glutathione with a carbonyl-reactive agent.
S3a , Supplementary Data 1 and 2 Figs. S8, S14) and by a quantitative 2,4-Dinitrophenylhydrazine (DNPH) colorimetric assay (Supplementary Fig. S3b  and Supplementary Data 2 Fig. S15 )  41. Both approaches revealed a significant increase in carbonylation in oxidized lysozyme as compared to non-oxidized control under indicated conditions. To identify and quantify carbonylated amino acids on the oxidized lysozyme, sequence database search and tandem mass spectrometry MS/MS analysis were performed (Supplementary Fig. S3c ). To search for oxidized peptides, we implemented userdefined variable modifications corresponding to a list of known carbonyl modifications from the literature  42  , which were derivatized with DNPH (Oxi-DNP method). For the MS/MS analysis, we labeled carbonylated amino acids with DNPH since DNPH-tagged-carbonylated peptides display the best results with this method.
S3f ). The key result is the H-bond network formed by glutathione ligands that protect the oxime bond between the Ao and a carbonylated residue on the protein. Together, these data justify the use of liganded AuNCs for the detection of protein carbonylation. MS-based detection of Au 15 SG 12 -Ao grafted on carbonylated amino acids within the oxidized lysozyme. To obtain evidence that Au 15 SG 12 -Ao is specific for carbonylated amino acids, i. e. that it is grafted directly on the carbonylated amino acid residue on the oxidized lysozyme, we next made attempts to measure directly the mass of Au 15 SG 12 -Ao bound to oxidized lysozyme. However, due to low levels of carbonylation (<1%) no NC attached to the oxidized lysozyme was detected by mass spectrometry (MALDI-MS technique), as evidenced by lack of any mass peak larger than that of the parent oxidized lysozyme.
As the IAM can react with the free thiols remaining on the carbonylated sites, the carbonyl modifications  5  derivatized with Ao-C3-thiol-IAM were implemented for the database search. Seven peptides with these modifications were identified using Protein prospector (Table  1 ). We found modifications corresponding to the direct binding of Au 15 SG 12 -Ao to carbonylated amino acids on W  28  , W 123 and I 98 -the same residues that we had identified as carbonylated in the previous analysis (Supplementary Fig. S3d ). These results indicate that the Au 15 SG 12 -Ao is grafted directly on the carbonylated sites of the lysozyme. Of note, S 100 , the carbonylated residue analyzed by molecular modeling (Fig. 2d ) was not found among Aobinding sites, likely due to degradation of the respective peptides.
S4  and Supplementary Data 1 Fig. S13 ). Visualization of the NC in the gels was achieved using fluorescence imaging, whereas the  proteins were visualized using Coomassie staining. Interestingly, NC and the lysozyme displayed opposing migration properties: while NC was detectable in the gel exposed to the conventional polarity, the lysozyme (both oxidized and non-oxidized) that has isoelectric point of 11. 35 under given conditions migrated into the gel only upon inversion of the polarity (Supplementary Fig. S4 ). Hence, we subjected a mixture of Au 15 SG 12 -Ao with the oxidized lysozyme along with corresponding controls to native PAGE (Fig. 3b  and Supplementary Data 1 Figs. S5-7 ), expecting to detect the stable complex formed between the protein and the NC in one of the two gels. Indeed, we observed co-migration of the Au 15 SG 12 -Ao and the oxidized protein, which was consistent with our result that NCs are grafted on the carbonylated sites of the protein (Fig.
Proteins were then transferred to a PVDF membrane the Trans-Blot Turbo Transfer System (BioRad) with 25 V constant (up to 1. 0 A) for 30 min. Membranes were stained with Red Ponceau dye (Sigma) to assess transfer efficiency and total protein loading. Membranes were then blocked with TBS-Tween 0. 05%-Milk 5% buffer for 30 min at room temperature with shaking. Next, membranes were incubated for 1h at RT with Streptavidin-Alexa Fluor 700 (Invitrogen) probes resuspended. At every step after blocking, membranes were washed 4 times 5 min with TBS-Tween 0. 05%. Finally, Typhoon™ FLA 9500 biomolecular imager (GE Healthcare) was used to measure fluorescence. Experiments were performed in triplicates. All quantifications were performed using ImageLab software (Bio-rad) and the statistical analysis was performed in GraphPad Software.
The S-lens RF was set at 90 (arbitrary unit). The Automatic Gain Control (AGC) target was 3 × 10 6 and the maximum injection time was set at 250 ms. Experiments were done in datadependent top 10 modes. The full MS scans were done over an m/z 300-1500 range with a resolution of 35000. For the data-dependent MS/MS scans, the resolution was set at 17500, isolation 2 m/z, with a normalized collision energy of 28 (arbitrary unit). To exclude the redundant processing of dominant ions and allow selection of low abundant oxidized peptides, a dynamic exclusion time of 20 s was set. MS proteomics. Peptide and protein identification and quantification. Fragmentation data were converted to peak lists using PAVA RawRead and searched against sequences of Gallus Gallus (Chicken) proteins contained in the Swissprot human database (downloaded 2017.
Acknowledgements This research was supported by the project  STIM-REI , Contract Number:  KK. 01. 1. 1. 01. 0003 , funded by the  European Union  through the  European Regional Development Fund-the Operational Programme Competitiveness and Cohesion   2014-2020 (KK. 01. 1. 1. 01 ). V. B. K. , M. P. B. , and H. F. acknowledge computational facilities of the  HPC computer  within the  STIM-REI  project , Doctoral study of Biophysics at University of Split  as well as  Prof. Miroslav Radman at MedILS  and  Split-Dalmatia County  for support. M. G. acknowledge the  French National Research Agency  for the financial support (Grant Agreement  ANR-18-CE29-0002-01 HyLOxi ). We would also.
The Innovative Medicines Initiative -Applied Public-Private Research enabling OsteoArthritis Clinical Headway (IMI-APPROACH) consortium has set up a broad database of different OA patients and a longitudinal cohort to combine conventional and new disease markers, and identify different OA phenotypes. The APPROACH cohort study is a 2-year observational study using a unique, multistep selection of participants, to include people with an increased likelihood of structural progression and/or pain progression. 4 Structural progression was defined as a reduction in Joint Space Width (JSW) of ≥0. 3 mm per year over a period of 2-3 years (0. 7 mm has been described as the minimal detectable difference in radiographic JSW). 5  Pain progression was defined as at least one of the three following: Knee Injury and Osteoarthritis Outcome Score (KOOS) pain (on a 0-100 point scale; 100 being the worst) increase ≥5 points/year with a pain level ≥40 at 2 years, or KOOS pain increase ≥10 points/year with a pain level ≥35 at 2 years or a pain level ≥40 at both baseline (BL) and 2 years.
Participants with the highest rank were selected for a screening visit and ranked again using collected up-to-date information on disease status (demographics, radiographs and questionnaires). Then 75% of the screened participants with the highest rank (most likely to progress) were included in the study. As a consequence, each included participant was assigned a S progression score, P progression score and a ranking score. The predictions of the final ML model are based on cross-sectional data of patients at the moment of or shortly before inclusion in the cohort, viz. BL data. Participants are subsequently followed for 2 years which is at present ongoing, to study actual progression with the use of a large number of conventional and exploratory measures. Irrespective of the actual progression, this analysis provides the clinical characteristics (demographics, radiographic features and pain severity) of the selected patients in the IMI-APPROACH prospective cohort in relation to their S progression, P progression and ranking score.
Statistical analysis IBM SPSS Statistics V. 25. 0. 0. 2 was used for statistical analysis. BL data are presented in relation to the S progression, P progression and final ranking score. Pearson correlation coefficient was used to determine correlations between progression/ranking scores and clinical, structural and demographic variables. Correlations were considered weak when r<0. 3, moderate when 0. 3≥r <0. 5, and strong when r≥0. 5. Additionally, patients were partitioned into three equal size groups (tertiles) with low, medium, and high values of the scores. BL Osteoarthritis Osteoarthritis Osteoarthritis clinical, structural and demographic characteristics were compared between patients with lowest (one-third) or highest (one-third) scores using T-test (continuous variables) or χ 2 test (categorical variables).
Acknowledgements We thank  M. Melief  for performing the KIDA analysis on all radiographs. Contributors EMvH, MK, FJB, IKH, and FB were responsible for acquisition of data. EMvH and PMJW were responsible for all analyses. EMvH, CL, SM, JB, PW, PMJW, and FL contributed to interpretation of data and were responsible for the first draft, all 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 contribution from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013 ) and EFPIA companies' in kind contribution. See www. imi. europa. eu and www. approachproject. eu.
The red lines correspond to a polar dust obscuration of the accretion disk emission with an SMC extinction curve, the dust emission is the sum of torus and polar dust emission at 100 K (red solid line) and 500 K (dashed line). The green line corresponds to a polar dust obscuration with a G04 extinction curve. All models are normalized to the same energy emitted by dust. The SED of the model without polar dust is shown as a blue short-dashed line, in this case the dust emission comes only from the dusty torus and the emission of accretion disk is unabsorbed along the line of sight. sources. For this study, we use the X-CIGALE code 1  that accounts for polar dust modeled as a simple dust screen in front of the accretion disk and a dust extinction curve. We aim to test the presence of polar dust and to characterize its extinction curve.
X-CIGALE modeling CIGALE  (Noll et al. 2009; Boquien et al. 2019) , along with its X-ray extension X-CIGALE  (Yang et al. 2020) , is a very versatile code aimed at generating spectral models to fit the observed SEDs of extragalactic systems. It combines a stellar SED with a dust component emitting in the IR and conserves the energy balance between dust absorbed emission and its thermal reemission. An AGN component can be added with its emission also modeled on the basis of the conservation of energy. The relative contribution of the AGN, f AGN , is measured as the fraction of total dust luminosity coming from the dusty torus and dust in polar regions (if present). Recently,  Yang et al. (2020)  developed a new branch of CIGALE, named X-CIGALE, with a phenomenological modeling of the X-ray emission to account for X-ray fluxes in the fits of the SED.
It is this version of the code that we use in this work. et al. (2020)  implemented a clumpy two-phase torus model, SKIRTOR  (Stalevski et al. 2012 (Stalevski et al. , 2016)) , based on the radiation transfer code, SKIRT  (Baes & Camps 2015). To account for the possible presence of dust in the polar regions that is not included in SKIRTOR, a new component called "polar dust" is added to the SKIRTOR model as a homogeneous dust screen in the foreground of the accretion disk characterized by different extinction curves. Energy conservation is also applied to this new component, and the re-emission of dust in the polar regions is modeled as a modified black body. In this work, we use these new developments to model a BLAGN sample and we test the need to introduce any polar dust contribution. With our sample being X-ray selected, we ran X-CIGALE to fit simultaneously X-ray and UV to IR data.
(2000) , and the absorbed energy is re-emitted using  Dale et al. (2014)   2  templates parametrized by a single parameter α representing the relative contribution of different local heating conditions. Following  Yang et al. (2020) , the AGN type 1 component is modeled by fixing the viewing angle to 30 degrees. All the other fixed input parameters describing the torus model of SKIR-TOR are fixed as in  Yang et al. (2020)   1. X-CIGALE modules and input variable parameters used to create the stellar and AGN components of the composite models. We refer to  Yang et al. (2020)  for the description and values of the input fixed parameters which are not described here. See text for details. the disk continuum emission from SKIRTOR with four powerlaws to cover the full range from 0. 008 µm to 1000 µm.
Near-IR photometry (J,H,K) is available for 836 sources from the Visible and Infrared Survey Telescope for Astronomy (VISTA;  Emerson et al. (2006). Mid-IR photometry comes from Spitzer (IRAC and MIPS) and allWISE  (Wright et al. 2010)  datasets. Spitzer data are provided by the HELP collaboration  (Shirley et al. 2021 ). The wavelength coverage of WISE and IRAC is crucial to identify AGN and we reduce the sample to the 1523 sources detected with either IRAC1 or W1, and IRAC2 or W2, by order of priority. We ensure a mid-IR coverage by selecting sources with at least one measured flux in either W3, W4, or MIPS1 3. We are left with 1275 sources. Herschel/SPIRE fluxes for 824 of these sources come from the HELP project 4. Only SPIRE fluxes were included in the analysis because of the much lower sensitivity of the PACS observation in the XMM-XXL field  (Oliver et al.
Statistical corrections from a mock analysis In order to further check the validity of our results on the introduction of the polar dust component, we performed a specific mock analysis with the full dataset. We used the X-CIGALE option to create a mock dataset from the best SED of each fit for the three subsamples of sources without polar dust, with polar dust modeled with the SMC law, and with polar dust modeled with the G04 law. The code uses the best flux of each source and adds a random noise extracted from a Gaussian distribution with the same standard deviation as for the observed flux. We merged the three datasets in a single mock catalog. This simulated catalog is fitted with the same strategy as for the observed sample (cf. Sect. 3. 2). We performed the selection of sources as described in Sect.
Summary In this work, we investigate the presence of dust along the line of sight of BLAGN by using the impact of adding polar dust reddening to their UV to IR continuum. We fit the SEDs of 1275 BLAGN in the XMM-XXL field with X-CIGALE. The code includes a simple model of polar component based on a screen geometry and an extinction curve, which can be chosen either steep (SMC curve of  Pei (1992))  or almost flat in the UV-optical range (G04 extinction curve of  Gaskell et al. (2004) ). Both curves have a very different impact on the reddening of the AGN continuum, which allows us to compare different configurations. We found a similar fraction of sources that are better fitted (or not) with the introduction of a polar dust component. A mock analysis conducted to understand the performance of our classification led us to conclude that our sources are probably equally distributed between the two categories.
Acknowledgements. The project has received funding from  Excellence Initiative of Aix-Marseille University -AMIDEX , a  French 'Investissements d'Avenir' programme. GM acknowledges support by the  Agencia Estatal de Investigacíon ,  Unidad de Excelencia María de Maeztu , ref. MDM-2017-0765. M. S. acknowledges support by the  Ministry of Education, Science and Technological Development of the Republic of Serbia  through the contract no. 451-03-9/2021-14/200002  and by the  Science Fund of the Republic of Serbia ,  PROMIS 6060916 , BOWIE.