dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability The authors declare that all data supporting the findings of this study are available in the article and its Supplementary Information Files, or upon request from the corresponding author. The data underlying all the figures and Supplementary Table  1  and Supplementary Table  2  are provided as a source data file. The transcriptomic data reported in this study have been deposited in the Gene Expression Omnibus (GEO) database under accession no. GSE183598. Metabolomics data have been deposited to the EMBL-EBI MetaboLights database (https://doi. org/10. 1093/nar/gkz1019, PMID:31691833) with the identifier MTBLS3121. The complete dataset can be accessed here. Source data are provided with this paper.
The raw data were fitted with ZView software by using an equivalent circuit consisting of a CPE and a resistor connected in parallel. Capacitances C were calculated according to C = R (1-n)/n × Q 1/n. R is the resistance in Ω and Q has the numerical value of the admittance at ω = 1 rad s -1. The variable n is dimensionless and it shows the deviation of the CPE from the behavior of an ideal RC unit, for which n = 1. NMR Spectroscopy: High-resolution 1 H,  13  C, 23 Na,  27  Al MAS NMR spectra on the Li bearing samples were recorded to collect structural information (Bruker Avance III 500 spectrometer, 11. 4 Tesla magnet). Bruker 2. 5 mm MAS probes was used that was operated at a spinning speed of 25 kHz. MAS rotors were filled with appropriate tools where the powder was hand-pressed firmly into the rotors.
Acknowledgements The authors thank  Professor W. Gössler  and  S. Meschnark  for the elemental analysis ( Karl Franzens University Graz ). Furthermore, the authors thank  Brigitte Bitschnau  for XRD and  Josefine Hobisch  for TGA and DSC measurements. Financial support by the  Deutsche Forschungsgemeinschaft  ( DFG WI3600 4-1  and  HA6966 1-2 ), the  Austrian Federal Ministry of Science, Research and Economy (BMBFW) , and the  National Foundation for Research, Technology and Development  is gratefully acknowledged. In addition, the study received considerable funding from the  European Union 's  Horizon 2020 research and innovation program  under the grant agreement no. 769929. The authors also thank the  FFG (K-project Safe Battery-grant  no. 856234  and project SolaBat-grant no. 853627 ) for financial support as well as the  Swedish Energy Agency  (grant no.
Strengths and limitations of this study ► The proposed scoping review will allow for comprehensive identification of the outcome measures (core set items) and relevant domains, which have been incorporated in the assessment of limited cutaneous systemic sclerosis (SSc). ► The limitations of the scoping review include the limited number of databases, the language and search terms used. ► This scoping review may also under-represent observational studies that do not explicitly mention the words 'limited SSc' or 'limited cutaneous SSc' in their title or abstract. Open access (SLS studies (Scleroderma Lung Study I and II)  6 7  and SENSCIS trial  8  (Safety and Efficacy of Nintedanib in Systemic Sclerosis). Moreover, recent studies focusing on quality of life have demonstrated that patients with lcSSc experience a significant impairment in their daily quality of life and that patients' experiences of lcSSc have been largely overlooked.
The endorsement to define these two subsets (limited vs diffuse) of the disease within the 1988 classification criteria was based on prognostic data and defined by the extent of skin fibrosis involvement. This was a historical milestone in the nosology of SSc. Examining articles published before 1988 could lead to the inclusion of outcomes used to assess populations that would not match with the contemporary definition of lcSSc and have therefore not been included. After 1988, the term CREST/CRST (Calcinosis-Raynaud's phenomenon-Sclerodactyly-Telangiectasia) syndrome Open access persisted and overlapped with lcSSc. Based on this, articles only mentioning CREST/CRST in their title and abstract after 1988 will also be considered for full-text review, and full-text assessment will confirm whether the population studies match with the contemporary definition of lcSSc.
Reporting of protocol and study records This study protocol follows PRISMA-ScR guidelines  35  and PRISMA guidelines for the publication of systematic 6 Lescoat A, et al. BMJ Open Open access review protocols,  36  with specific adaptations for this scoping review protocol. Data management will be housed through Covidence (https://www. covidence. org/ home), under the supervision of DK and MHB. DR, JP, MH, RS, AL and FZ will screen citations and review for eligibility and inclusion based on the eligibility criteria and the article selection template (tables 1 and 2). AL will assess all the titles (T) and abstracts (A), and the other five reviewers will evaluate 1/5th of T/A to ensure that all articles will be double checked. Inter-rater agreement will be evaluated using Cohen's Kappa statistics.
A first test of agreement will be performed based on 50 citations. If Kappa coefficients are under 0. 8, we will evaluate the disagreements and understand the reason to correct misunderstanding and ensure consistency for the rest of the review process. T and A will then be reviewed for the entire article list. Any disagreements between reviewers will be reviewed and resolved by DK and/or MHB. If uncertainty persists, the manuscript will be included for comprehensiveness. Where there is lack of data clarity pertaining to exclusion criteria in manuscripts, mentors will be contacted to discuss this issue. Agreement between pairs for overlapping citations will also be assessed using Cohen's Kappa statistics at the end of the process. After article selection, the data extraction template (table  3 ) for full-text review will be evaluated by two reviewers on a sample of 20% of included studies to adjust its sensitivity.
► Although comprehensive, this study design has limitations regarding the number of databases, the language and search terms used and may underrepresent observational studies that do not explicitly mention the word 'limited SSc' or 'limited cutaneous SSc' in their title or abstract. ► By focusing on articles published after 1988, this scoping review may not capture the outcome measures used to assess equivalent subsets of lcSSc before this date, but this will ensure that the population included match with the contemporary definition of lcSSc. ► For this first phase of the initiative, we are seeking to identify relevant domains and outcomes as opposed to evaluate their specific properties in lcSSc. Nevertheless, the several clinical trials to be evaluated will not report results solely in lcSSc patients, which constitutes a limitation of our protocol.
► Regulatory agencies emphasise outcomes should reflect how patients feel, function and/or survive. With this in mind, as the main objective is to identify domains and outcomes that could be incorporated in a combined response index, we have not included congress databases or studies on exploratory biomarkers or epigenetic/genetic studies. Such a selection will limit the comprehensiveness of this scoping review but will insure its coherence with the global objective of the project. Regarding this main objective, to remain consistent with the comprehensive concept of scoping review, and considering that we are not performing a systematic review or metaanalysis, we will not evaluate quality appraisal, and this could be considered as a limitation of this protocol.
All authors critically reviewed drafts and edited the manuscript. Funding This project has received funding from  SRUK (Scleroderma and Raynaud UK) & WSF  ( World Scleroderma Foundation ) ( UH&UHR1 ) and additional support is on May 26, 2021 by guest. Protected by copyright. provided by the  National Institutes of Health/NIAMS  grant to  Dr DK  ( K24 AR063120 ). Dr DR was funded by the  NIH/NIAMS   T32 grant  ( AR007080 ). Dr AL  was funded by the  French network of the University Hospitals HUGO (Hôpitaux Universitaire du Grand Ouest)  ( AAP JCM2020 ) and by the 2020 CORECT grant from the  Rennes University Hospital (France). Competing interests MH has received speaker honoraria (<$ 10 000 ) from  Actelion pharmaceuticals , JP has received speaker's honoraria and research grant support (>$ 10 000 ) from  Actelion pharmaceuticals.
, 2016; Vitkup et al. , 2003). Regulatory mutations are enriched in a predicted TDH3 regulatory network Because of the key role transcription factors play in the regulation of gene expression, and because transcription factors have been shown to be a source of trans-regulatory variation in natural populations  (Albert et al. , 2018; Lewis et al. , 2014) , we asked whether trans-regulatory mutations affecting PTDH3-YFP expression were enriched in genes encoding transcription factors. We found that 5 (7. 7%) of the 65 trans-regulatory coding mutations mapped to the coding sequence of one of the 212 genes predicted to encode a transcription factor in the YEASTRACT database  (Teixeira et al. , 2018) , but this was not significantly more than the 5. 6% of non-regulatory coding mutations mapping to these genes (G-test: P = 0.
52). Not all transcription factors are expected to regulate expression of TDH3, however, so we also tested for enrichment of trans-regulatory mutations among transcription factors specifically predicted to regulate TDH3. Using information consolidated in the YEASTRACT database  (Teixeira et al. , 2018)  that supports evidence of a transcription factor binding to a gene's promoter and regulating its expression, we constructed a network (Figure  4 ) of potential direct regulators of TDH3 as well as potential direct regulators of these direct regulators (1 st and 2 nd level regulators of TDH3) and asked how often the trans-regulatory mutations we identified mapped to these genes. We found that 4 trans-regulatory mutations mapped to three genes in this network, with 2 mutations affecting the 1 st level regulator TYE7, 1 mutation affecting the 1 st level regulator GCR2, and 1 mutation affecting the 2 nd level regulator TUP1 (Supplementary File 6).
This number of mutations mapping to genes in the predicted TDH3 regulatory network was 12-fold greater than expected by chance (6% for trans-regulatory vs 0. 5% for non-regulatory mutations; G-test, P = 0. 0037), thus the inferred regulatory network had predictive power even though the vast majority of trans-regulatory coding mutations (61 of 65, or 94%) mapped to other genes. Only one of these other trans-regulatory mutations mapped to a transcription factor. This mutation was a nonsynonymous substitution affecting ROX1, which is predicted in the YEASTRACT database to directly regulate expression of the indirect TDH3 regulator TUP1. In other words, ROX1 is predicted by existing functional genomic data to be a 3 rd level regulator of TDH3 (Figure  4 ). With no other transcription factors harboring a trans-regulatory mutation in our dataset, this result suggests that mutations in transcription factors located more than three levels away from TDH3 in its transcriptional regulatory network are unlikely to be sources of new expression changes driven by the TDH3 promoter.
A 488 nm laser was used for excitation and the YFP signal was acquired with a 530/30 optical filter. Each well was sampled for 2 seconds, yielding fluorescence and cell size measurements for at least 5000 events per well. Flow cytometry data were analyzed using custom R scripts (Supplementary File 12) as described in  Duveau et al. (2018). First, events that did not correspond to single cells were filtered out using flowClust clustering functions. Second, fluorescence intensity was scaled by cell size in several steps. For Figures  1B-D , these values of fluorescence relative to cell size were directly used for subsequent steps of the analysis. For other figures, these values were transformed using a log-linear function to be linearly related with YFP abundance. Transformations of fluorescence values were performed using the relationship between fluorescence levels and YFP mRNA levels established in  Duveau et al.
BSA-Seq procedure To identify mutations associated with fluorescence levels in EMS-treated mutants, we used bulk-segregant analysis followed by Illumina sequencing (BSA-Seq). BSA-Seq data corresponding to the 6 mutants from  Gruber et al. (2012)  were collected together with the BSA-Seq dataset published in  Duveau et al. (2014). For the other 76 mutants (from  Metzger et al. , 2016) , BSA-Seq data were collected in this study in several batches (see Supplementary File 13) using the experimental approach described in  Duveau et al. (2014)  (with few modifications). First, each EMS-treated mutant (MAT ura3d0 ho::PTDH3-YFP ho::KanMX) was crossed to the mapping strain YPW1240 (MATa ura3d0 ho::PTDH3-YFP ho::NatMX4 mata2::yEmRFP-HygMX) that contained the FASTER MT system from  Chin et al. (2012)  used to tag diploid and MATa cells with a fluorescent reporter.
18 libraries sequenced in run 54374 were not analyzed in this study. Analysis of BSA-Seq data Demultiplexing of sequencing reads and generation of FASTQ files were performed using Illumina bcl2fastq v1. 8. 4 for sequencing runs 45300 and 45301 and bcl2fastq2 v2. 17 for runs 54374 and 54375. The next steps of the analysis were processed on the Flux cluster administered by the Advanced Research Computing Technology Services of the University of Michigan (script available in Supplementary File 15). First, low quality ends of reads were trimmed with sickle (https://github. com/najoshi/sickle) and adapter sequences were removed with cutadapt  (Martin, 2011). Reads were then aligned to the S288c reference genome (https://www. yeastgenome. org/, R64-1-1 release to which we added the sequences corresponding to PTDH3-YFP, KanMX and NatMX4 transgenes, available in Supplementary File 11) using bowtie2  (Langmead & Salzberg, 2012)  and overlaps between paired reads were clipped using clipOverlap in bamUtil (https://github.
com/statgen/bamUtil). The sequencing depth at each position in the genome was determined using bedtools genomecov (https://github. com/arq5x/bedtools2). For variant calling, BAM files corresponding to the low fluorescence bulk and to the high fluorescence bulk of each mutant were processed together using freebayes (https://github. com/ekg/freebayes; Garrison & Marth, 2012)  with options -pooled-discrete --pooled-continuous. That way, sequencing data from both bulks were pooled to increase the sensitivity of variant calling and allele counts were reported separately for each bulk. To obtain a list of mutations present in each mutant strain, false positive calls in the VCF files generated by freebayes were then filtered out with the Bioconductor package VariantAnnotation in R (Supplementary File 16).
5 ml of YPD (same plate design as used for the flow cytometry assays) and grown overnight at 30°C. 0. 2 ml of cell culture from each well was then mixed with 46 µl of 80% glycerol in 96well plates and stored at -80°C. The fluorescence of these strains was quantified by flow cytometry as described above to assess the impact of RAP1 and GCR1 mutations on PTDH3-YFP expression (expression data for each mutant can be found in Supplementary File 19). In our mutagenesis approach, we introduced a mutation that impaired the target PAM sequence in all RAP1 and GCR1 mutants. To determine the effect of this mutation alone, we generated strains YPW2701 and YPW2732 that carried the PAM mutation in the RAP1 terminator or in the GCR1 intron, respectively, without any other mutation in RAP1 or GCR1. The fluorescence level of these two strains was not significantly different from the fluorescence level of the progenitor strain YPW1139 in flow cytometry assays.
For each experiment, all strains were grown in parallel in culture tubes containing 5 ml of YPD and incubated at 30°C for 16 hours. Each sample was diluted to 1-2 x 10 7 cells/mL in PBS prior to measurement. At least 5 x 10 4 events were recorded for each sample using a 488 nm laser for YFP excitation and a 525/50 optical filter for the acquisition of fluorescence. At least 5 x 10 4 events were recorded for each sample. Flow cytometry data were then processed in R using functions from the FlowCore package and custom scripts available in Supplementary File 12. After log-transformation of flow data, events considered to correspond to single cells were selected on the basis of their forward scatter height and width (FSC-H and FSC-W). Fluorescence values of single cells were then normalized to account for differences in cell size.
TDH3 regulatory network The network of potential TDH3 regulators shown on Figure  4  was established using data available in July 2019 on the YEASTRACT (www. yeastract. com) repository of regulatory associations between transcription factors and target genes in Saccharomyces cerevisiae  (Teixeira et al. , 2018). We used the tool "Regulation Matrix" to obtain three matrices in which rows corresponded to the 220 transcription factor genes in YEASTRACT and columns corresponded to the 6886 yeast target genes included in the database. In the first matrix obtained using the option "Only DNA binding evidence", an element had a value of 1 if the transcription factor at the corresponding row was reported in the literature to bind to the promoter of the target gene at the corresponding column and a value of 0 otherwise.
Finally, an element had a value of 0 in both matrices if perturbation of the transcription factor at the corresponding row was not reported to alter expression of the target gene at the corresponding column in the literature. We then used a custom R script (Supplementary File 16) to generate a smaller matrix that only contained first level and second level regulators of TDH3 and TDH3 itself. A transcription factor was considered to be a first level regulator of TDH3 if a regulatory association with TDH3 was supported both by DNA binding evidence and expression evidence. A transcription factor was considered to be a second level regulator of TDH3 if a regulatory association with a first level regulator of TDH3 was supported both by DNA binding evidence and expression evidence. The network shown on Figure  4  was drawn using Adobe Illustrator based on regulatory interactions included in the matrix of TDH3 regulators (in Supplementary File 11).
Samples were analyzed on a BD Accuri C6 flow cytometer with a 488 nm laser used for excitation and two different optical filters (510/10 and 585/40) used to separate YFP and GFP signals. FCS data were analyzed with custom R scripts using flowCore and flowClust packages (Supplementary File 12) as described in  Duveau et al. (2018). First, we filtered out artifactual events with extreme values of forward scatter or fluorescence intensity. Then, for each sample we identified two clusters of events corresponding to [YFP+] and [GFP+] cells using a principal component analysis on the logarithms of FL1. H and FL2. H (height of the fluorescence signal captured through the 510/10 and 585/40 filters, respectively). Indeed, [YFP+] cells tend to have lower FL1. H value and higher FL2. H value than [GFP+] cells and these two parameters are positively correlated.
Acknowledgments We thank  Gaël Yvert  and  Mark Hill  for helpful comments on the manuscript, the  University of Michigan sequencing core and University of Michigan  flow cytometry core for research support, and the  National Institutes of Health  ( R01GM108826  and  R35GM118073  to P. J. W. ),  European Molecular Biology Organization  ( EMBO ALTF 1114-2012  to F. D. ),  National Science Foundation  ( MCB-1929737  to P. J. W. ),  NIH   Genetics Training grant  ( T32GM007544  to P. V. Z. ),  NIH   Genome Sciences Training Grant  ( T32HG000040  to B. P. H. M and M. A. S. ), and the  Michigan Life Sciences Fellow program  (M. A. S. ) for funding.
A telephone in the central team will be 24/7 available in case online assignation was not possible. The generated randomization lists will be securely incorporated within the web trial database, and allocation concealed until the point of the next randomization. The designated members of the research staff at each site will be responsible for carrying out the randomization process using a secure electronic system within the trial database. Randomization will not take place until after informed consent has been given and the participant is ready to receive therapy. A reliable manual back-up system will also be available. If the center's internet connection is unavailable at the time of randomization, the screening details can be provided to staff at the FI+12-CTU by phone. At the CTU, staff will verify eligibility and perform the randomization using the online system.
Case report forms All study data will be collected by the clinical study staff using designated source documents. Electronic case report forms (eCRFs) will be developed for the collection of the trial's data. Those centers without the capacity of using eCRF will use paper-based CRFs. Study data can be entered directly into the eCRFs during a study visit and uploaded into the server. In sites where paper-based CRFs will be used, data will be then entered into the electronic database as promptly as is feasible. After the introduction, data will be double-checked by a senior researcher when possible. CRFs and laboratory reports will be reviewed primarily by the site clinical team who are responsible for ensuring that they are accurate and complete. The eCRFs will be the data source since they are considered to have more information than the medical routine records.
Plans to promote participant retention and complete follow-up {18b} SPIRIT guidance: Plans to promote participant retention and complete follow-up, including list of any outcome data to be collected for participants who discontinue or deviate from intervention protocols. Participants who do not attend any of the scheduled visits will be contacted telephonically by study personnel and through the community to assess the vital status and compliance with study interventions. All efforts will be done to assess the survival of the patients. After discharge, TB-T will be provided up to the next visit, if randomized to TB-T, and after each visit up to the following visit. All patients will be scheduled equally for follow-up visits. Follow-up will end 12 months after the initial enrollment.
Data management {19} SPIRIT guidance: Plans for data entry, coding, security, and storage, including any related processes to promote data quality (eg, double data entry; range checks for data values). Reference to where details of data management procedures can be found, if not in the protocol. A centralized computerized database, managed by the CTU in REDCap, will be the central data repository for all sites participating in the trial. Each site participating in the trial will be responsible for data entry and rst cleaning. Data collected electronically will be transferred from the sites to the CTU using an own secure server, allocated in Hospital 12 Octubre (Madrid) and maintained by the Clinical Trials and CTU in the Hospital 12 Octubre. Sites will be able to self-edit, make online quality check reports, and be informed of the data collected during the clinical trial from their own site.
Local investigators will have access during data-collection time upon request and under justi cation to the complete pseudonymized database exportation. User-speci c usernames and passwords are required to log onto the database. User rights will be provided to study staff, PIs, and coinvestigators at the level appropriate for each individual's job description by the central CTU data managers. After principal results, addressing primary and secondary objectives will be published, data repository will be public-available by concrete permission of the CTU and data will be pseudo-anonymized and encrypted. Those interested in having the database or any of its subsets should provide a concrete research proposal that may be accepted under citation condition. The local PIs will maintain, and store securely, complete, accurate and current study records throughout the study.