dataesr/scientific-paragraphs-categorization huggingface dataset:
We used JunctionSeq to examine all OAS1 transcript junctions (annotated on Ensembl) in these AA cells in the presence or absence of IFN treatment (fig. S5, A to C ). Accordingly, we were unable to detect the specific junction (J080) between exons 5 and 7 that specifies the expression of prenylated OAS1 in these cells (fig. S5, A to C ). We therefore applied this method to RNAseq data from 499 hospitalized UK COVID-19 patients with known disease outcomes (ISAR-IC4C) (fig. S6, A to C ). We defined severe outcomes as intensive care unit (ICU) admission and/or death, and mild outcome patients as those who were hospitalized but not admitted to an ICU. All patients expressed detectable OAS1, but 42. 5% of individuals (212/499) did not express p46. The absence of prenylated OAS1 was associated with more severe disease (Fig.
Accordingly, OAS1 from P. kuhlii and C. dromedarius have CAAX boxes and both instigated potent blocks to SARS-CoV-2 (Fig. 6C ). Notably, the P. kuhlii C terminus is shorter than human p42, reinforcing the notion that most of the extended C terminus of p46 is not necessary for antiviral activity (fig. S7A ). Crucially, this means that all of the species believed to harbor either OC43 or MERS-CoV en route to emergence in humans express prenylated OAS1 proteins that could credibly have selected for the maintenance of PDE expression in these viruses. The extreme sensitivity of SARS-CoV-2 to prenylated OAS1 also led us to investigate whether horseshoe bats, the likely source of SARS-CoV-2, have a prenylated OAS1 defense. There is a paucity of mRNA sequence data available for horseshoe bats, and we were unable to find OAS1 database entries from the likely bat hosts of the precursors of SARS-CoV-2 [Rhinolophus affinis or Rhinolophus pusillus (68)] and SARS-CoV (Rhinolophus sinicus).
Thus, we analyzed OAS1 transcripts from the greater horseshoe bat, Rhinolophus ferrumequinum. We were unable to find a transcript or exon encoding a prenylated OAS1 in R. ferrumequinum. Indeed, all Ensembl and National Center for Biotechnology Information (NCBI) database entries specified nonprenylated proteins. When we examined the genomic region where the prenylation signal should reside (on the basis of synteny and homologous flanking sequences), retrotransposition of a long terminal repeat (LTR) sequence was evident, and this ablated the CAAX-box motif, preventing the expression of prenylated anti-CoV OAS1 in these bats (Fig. 6D ). We searched for this insertion in 44 available bat genome sequences and identified the same insertion only in members of the Rhinolophoidea superfamily (including Rhinolophus, Hipposideros, and Megaderma species), indicating that this ancient retrotransposition insertion occurred ~58 to 52 million years ago within this bat superfamily.
Retroviral vectors and cell modification The lentiviral vector pSCRPSY (KT368137. 1) has been previously described  (5). pLV-EF1a-IRES-Puro (Addgene plasmid #85132) or pLV-EF1a-IRES-Blast (Addgene plasmid #85133) were modified by polymerase chain reaction (PCR) amplifying the TagRFP ORF (using pSCRPSY as template) flanked by directional SfiI sites, which were further flanked by BamHI and EcoRI restriction sites (forward oligo: 5′-CTC TCG GAT CCG GCC GAG AGG GCC ATG AGC GAG CTG ATT AAG-3′ and reverse oligo: 5′-CTC TCG AAT TCG GCC AGA GAG GCC TCA CTT GTG CCC CAG-3′), and the BamHI-EcoRI fragment was subcloned into the vectors to create the modified pLV-EF1a-IRES-Puro-SfiI-TagRFP or pLV-EF1a-IRES-Blast-SfiI-TagRFP constructs. The cDNA corresponding to the ORFs of the following OAS genes (GenBank accession number): OAS1p46 (NM_016816), human OAS3 (NM_006187), mouse Oas1A (NM_145211), bovine OAS1X (NM_178108), bovine OAS1Y (NM_001040606), bovine OAS1Z (AY650038), P.
kuhlii (XM_036409709. 1), C. dromedarius (XM_031443284), R. ferrumequinum OAS1 (short isoform: XM_033097132 / long isoform: ENSRFET00010016745), and P. alecto (NM_001290162) were synthesized as gene blocks with flanking SfiI sites (IDT DNA), and the SfiI fragment was subcloned into the modified pLV-EF1a-IRES-Puro-SfiI plasmid. To generate the human OAS1p42 sequence (in accordance with GenBank accession NM_002534), OAS1p46-C397A, and OAS1p42-CTIL sequences, the pLV-SfiI-OAS1p46 lentiviral vector plasmid was modified by overlap extension PCR (using primer pair 5′-CTC TCT GGC CGA GAG GGC CAT GAT GGA TCT CAG AAA TAC CCC AG-3′ and 5′-TCT CTC GGC CAG AGA GGC CTC AAG CTT CAT GGA GAG GGG CAG GGA TGA ATG GCA GGG AGG AAG CAG GAG GTC TCA CCA GCA GAA TCC AGG AGC TCA CTG GG-3′ for OAS1p42, primer pair 5′-CTC TCT GGC CGA GAG GGC CAT GAT GGA TCT CAG AAA TAC CCC AG -3′ and 5′-TCT CTC GGC CAG AGA GGC CTC AGA GGA TGG TGG CGG TCC AGT CCT CTT CTG CCT GTG GG -3′ for OAS1p46-C397A, and primer pair 5′-CTC TCT GGC CGA GAG GGC CAT GAT GGA TCT CAG AAA TAC CCC AG -3′ and 5′-TCT CTC GGC CAG AGA GGC CTC AGA GGA TGG TGC AAG CTT CAT GGA GAG GGG CAG GGA TGA ATG GCA GGG AGG AAG CAG GAG GTC TCA CCA GCA GAA TCC AGG AGC TC ACT GGG -3′ for OAS1p42-CTIL) and the respective SfiI fragments were subcloned in place of OAS1p46 in the pLV lentiviral vector plasmids described above.
Optimal quantitative PCR cycles were determined on a Quantstudio 3 (Thermo Fisher Scientific) using EvaGreen Plus (Biotium, 31077-T), 2× Phusion HF Mastermix, and P5/P3 Solexa primer. Final PCR products were purified using two rounds of ProNex Size selection. Libraries were quantified using a Qubit 4 Fluorometer (Thermo Fisher Scientific) and a TapeStation (Agilent Technologies). Each group of samples was pooled equimolarly and then mixed at the following proportions: 51. 7% OAS1 library pool, 38. 7% size-matched input library pool, and 8. 6% negative control IgG. The pooled pool was sequenced on a NextSeq 550 sequencer with a high-output cartridge. The raw data are available from the Gene Expression Omnibus (GEO) under accession number GSE182394.
readthedocs. io/en/latest/overview. html). Uniquely aligned reads were then used to extract the cross-link truncation site (position 1 relative to the 5′-end of the read start) using bedtools and htseq-clip/extract and quantified using htseq-clip/count. For peak calling, we used the R/Bioconductor DEWSeq package to identify significantly enriched sliding windows in OAS1 immunoprecipitated samples over the corresponding size-matched input control samples (adjusted P value < 0. 01, log2fold change > 2). The IHW R package was used for multiple hypothesis correction. To remove background signal resulting from nonspecific binding of RNA in our immunoprecipitation experiments, sliding windows harboring more cross-link counts (reads per million, log2-fold change > 2) in IgG samples compared with OAS1 immunoprecipitation samples were removed from further analysis.
For quantification of base composition around the start of the 5′-UTR sequence, reads mapping to the 5′-UTR were extracted using BBMap (https://jgi. doe. gov/data-and-tools/bb-tools/) with kmer = 25 mode. Then the 5′-most base of unique reads was extracted from aligned reads and the composition of bases at the 5′ start site and the 5′ off-template positions were quantified using bam-readcount (https://github. com/genome/bam-readcount). To assess enrichment of iCLIP reads mapping to each chromosome contigs, Samtools/idxstat was used to report alignment summary statistics of deduplicated reads. The ratio of the observed-toexpected read count was calculated using the samtools module of MultiQC software. To identify enriched motifs within iCLIP hits, 25 nucleotides either side of the peak in signal for each hit were taken as the input.
Synteny analysis The Ensembl web database was used for assessing the OAS1 locus genome synteny between the human genome (GRCh38. p13) and the available Rhinolophus species, R. ferrumequinum, genome (mRhiFer1_v1. p). The syntenic region between the human OAS1 exon 7 (ENSE00003913305) and the horseshoe bat genome was examined to identify a region in the latter genome sequence starting at position 7,833,728 of scaffold 25 of the mRhiFer1_v1. p primary assembly that lacked synteny to the human genome. Incidentally, the nonsyntenic region started inframe where the p46 "CTIL" encoding human sequence would have been. We extracted the 580-bp R. ferrumequinum sequence span up to where synteny resumes to the human genome and used hmmscan (HMMER 3. 2. 1) (93)  to search against the Dfam database (94) for transposable elements present in the sequence.
In silico genome screening To explore how far back in time this LTR insertion at the OAS1 locus took place, we used Database-Integrated Genome-Screening (DIGS) software  (96). DIGS uses a nucleotide or amino acid sequence probe to perform a BLAST similarity search through genome assemblies. We collected a set of 44 Chiroptera species genome assemblies to perform three in silico screens. We first used the nucleotide sequence of the syntenic region of R. ferrumequinum to human exon 7 (Ensembl) and the adjacent 580-bp region with the detected LTR insertion until homology resumes to the human genome as a probe. The DIGS screen was conducted using a minimum blastn bitscore of 30 and minimum sequence length of 30 nucleotides. Matches were aligned using MAFFT v7. 453 and inspected for covering all regions of the probe.
PDE analysis To examine the diversity of PDE proteins encoded by coronaviruses, we first constructed an HMMER protein profile. Two seemingly independently acquired PDEs are encoded by the NS2 of Embecoviruses (  62 ) and NS4b of MERS-like coronaviruses (62), respectively. Group A rotavirus (RVA) has also been described to encode a protein with a homologous PDE domain and similar biological function  (70). Finally, the AKAP7 mammalian protein holds a PDE domain that has been experimentally shown to complement the function of murine coronaviruses' NS2 activity  (69). We aligned the amino acid sequence of the PDE domains of the OC43 NS2 (AAT84352. 1), the MERS NS4b (AIA22866. 1), and the NS4b proteins of two more bat Merbecoviruses HKU5 (YP_001039965. 1) and SC2013 (AHY61340. 1), the AKAP7 proteins of Rattus norvegicus (NP_001001801.
1), Mus musculus (NP_001366167. 1), and humans (NP_057461. 2) (as their homology to CoV PDEs has been previously characterized) and the Rotavirus A VP3 protein (AKD32168. 1). The alignment was then manually curated using Bioedit on the basis of the homology described in the literature. The final alignment was used to produce an HMM profile using the HMMER suite (v3. 2. 1)  (93). All complete Coronaviridae sequences were downloaded from the NCBI virus online database  (97). Only sequences with an annotated host and length above 25,000 bp were retained and viruses of "severe acute respiratory syndrome-related coronavirus" species with a human host were excluded, producing a dataset of 2042 complete or near-complete coronavirus genomes. The EMBOSS getorf program was used to extract the translated sequences of all methionine starting ORFs with length >100 nucleotides from the filtered virus genome dataset.
ISG expression in gastrointestinal and respiratory tissue and an interferome database The six key cDNAs (UNC93B1, SCARB2, ANKFY1, NCOA7, ZBTB42, and OAS1) that were hits in our screens, SARS-CoV-2 cofactors (ACE2, TMPRSS2, and CSTL), and RNase L were examined for their transcript abundance across respiratory and gastrointestinal tissues using 17,382 RNA-sequencing datasets from the Genotype-Tissue Expression (GTEx) v8 database (GTEx Consortium, 2020). Gene expression is shown as log10 transform of transcripts per million. The respiratory tissue here includes lung and minor salivary gland tissues, and gastrointestinal tissue includes colon, esophagus, and small intestine tissues. To visualize the IFN responsivity in other datasets, an interferome database was used. Data from the Interferome v2.
Using alignment files as input, strand-specific splice-junctionlevel counts for each sample were generated using QoRTs (Quality of RNA-seq Tool-Set)  (99). QoRTs generates a set of nonoverlapping transcript features from the genome annotation, assigns a unique identifier to each feature, and generates counts for each annotated transcript subunit. To validate the method, we applied it to RNA-seq data from IFN-treated A549 cells, which have the AA genotype at Rs10774671  (29). We used the samples A549Cas9Clone1, 3, 4, 7, and 10 that were mock treated (NO IFN) or treated with IFN (IFNbeta) using data retrieved from the European Bioinformatics Institute (EBI) under project accession number PRJEB29677. On the basis of the presence or absence of p46 junction counts, mild and severe patient samples were subdivided further into p46-positive (mild and severe) and p46-negative (mild and severe) groups.
Analyses were implemented using IBM SPSS Statistics version 25 (IBM Corp. Armonk, USA). To detect whether one patient group stochastically expressed more of an OAS1 isoform than the other group, we used the nonparametric Mann-Whitney U test comparing patients classified as experiencing mild or severe COVID-19. Similarly, where multiple groups were compared (Fig. 4E , right) we used the nonparametric Kruskal-Wallis rank sum test followed by post hoc analysis using the Dunn test. The G frequencies in different populations (at Rs10774671) were extracted from the 1000 genomes project using ensembl. The populations considered were AFR (Africa), AMR (American), EAS (East Asian), EUR (European), and SAS (South Asian). These populations were further subdivided into ASW (African ancestry in SW USA), ACB (African Caribbean in Barbados), BEB (Bengali in Bangladesh.
ACKNOWLEDGMENTS This report is independent research that used data provided by the  Medical Research Council -funded  ISARIC4C  Consortium, which the Consortium collected under a research contract funded by the  National Institute for Health Research (NIHR). The views expressed in this publication are those of the author(s) and not necessarily those of the ISARIC4C consortium. We thank  P. Moss  and the  UK Coronavirus Immunology Consortium  ( UK-CIC ) for their guidance and suggestions;  V. Thiel ,  P. Collins ,  M. Peiris ,  B. Haagmans ,  A. Patel ,  A. Kohl ,  S. Neil ,  M. Bouloy ,  W. Barclay , and the  G2P-UK consortium  for viruses and cells;  A. Aziz ,  R. Gifford ,  A. Szemiel , and  W. Furnon  for technical assistance;  Nexcelom (Celigo)  for their technical assistance and support during the UK COVID-19 lockdowns (in 2020);  J.
SARS-CoV-2 CVR-GLA-1, SARS-CoV-2-ZsGreen in addition to the modified cell lines (described herein) and plasmids used for these modifications are available from S. J. W. under an  MTA  with the  University of Glasgow. Funding: This work was partly funded by  UKRI/NIHR  through the  UK Coronavirus Immunology Consortium  ( UK-CIC MR/V028448/1  to M. P. and S. J. W. ) and the  MRC  through the following grants:  MR/ K024752/1  (to S. J. W. ),  MC_UU_12014/10  (to M. P. and S. J. W. ),  MC_UU_12014/ 12  (to J. H. , D. L. R. , and S. K. ) and  MR/P022642/1  (to S. J. W. and S. J. R. ),  MR/V000489/1  (to E. C. Y. W. and R. J. S. ),  MR/S00971X/ 1  (to R. J. S. and E. C. Y. W. ),  MR/P001602/1  (to E. C. Y. W. ), and  MR/ V011561/1  (to P. J. L. ). Support was also provided by a  Wellcome Principal Research Fellowship   210688/Z/18/Z  (to P.
J. L. ), a  Wellcome Trust Fellowship   201366/Z/16/Z  (to S. J. R. ), a  Wellcome Investigator Award   209412/Z/17/Z  (to I. D. ), the  Addenbrooke's Charitable Trust  and the  NIHR Cambridge Biomedical Research Centre  (to P. J. L. ), support from the  German Research Foundation ,  Deutsche Forschungsgemeinschaft ; project number  406109949  (to V. H. ), and  German Federal Ministry of Food and Agriculture  through  BMEL Förderkennzeichen :  01KI1723G  (to V. H. ), and a  Daphne Jackson Fellowship  funded by  Medical Research Scotland  (to S. S. ). A. C. is supported by  MRC  grants  MR/R021562/1 ,  MC_UU_12014/10 , and  MC_UU_12014/12. J. Y. L. is funded by a  Medial Sciences Graduate Studentship ,  University of Oxford. L. I. is funded by  BBSRC DTP  scholarship number  BB/M011224/1. MAIC analysis was supported by the  SHIELD Consortium  ( MRC grant   MRNO2995X/1 ).
ISARIC4C is supported by grants from the  Medical Research Council  (grant  MC_PC_19059 ), the  NIHR  (award  CO-CIN-01 ) and by the  NIHR Health Protection Research Unit (HPRU) in Emerging and Zoonotic Infections at University of Liverpool  in partnership with  Public Health England (PHE) , in collaboration with  Liverpool School of Tropical Medicine  and the  University of Oxford  (award 200907),  NIHR HPRU in Respiratory Infections at Imperial College London  with  PHE  (award 200927),  Wellcome Trust and Department for International Development  ( 215091/Z/ 18/Z ), and the  Bill and Melinda Gates Foundation  ( OPP1209135 ), and  Liverpool Experimental Cancer Medicine Centre  ( C18616/ A25153 ),  NIHR Biomedical Research Centre at Imperial College London  ( IS-BRC-1215-20013 ),  EU Platform foR European Preparedness Against (Re-)emerging Epidemics (PREPARE)  ( FP7  project  602525 ) and  NIHR Clinical Research Network  provided infrastructure support for this research.
The novel coronavirus (COVID-19) pandemic outbreak is drastically shaping and reshaping many aspects of our life, with a huge impact on our social life. In this era of lockdown policies in most of the major cities around the world, we see a huge increase in people and professionals' engagement in social media. Online Social Networks are playing an important role in news propagation as well as keeping people in contact. At the same time, social media is both a blessing and a curse as the coronavirus infodemic has become a major concern, and is already a topic that needs special attention and further research. In this study, we publish a multilingual coronavirus (COVID-19) Instagram dataset that we have continuously collected during the first wave of the pandemic from 5 January 2020 to 30 May 2020.
The dataset contains 25. 7K posts, 829K comments, and 3. 2M likes in various subjects from different publishers such as 'public accounts', 'fake accounts (bots)', 'newsagencies', 'influencers', 'celebrities', 'business pages', etc. In addition to the dataset, this paper provides an analysis of the behaviour of the publishers. We study the behavioural aspects of the users in terms of their engagement, use of hashtags, activities, reactions as well as a full analysis of the published content related to the COVID-19. We believe this contribution helps the research community to better understand the dynamics behind this phenomenon in Instagram, as one of the major social media.
B. THE FIRST CORONAVIRUS WAVE The World Health Organization published the first disease outbreak news on 5th January 2020  [7] , containing risk assessment and advice  [8]. On 9th March 2020, Italy declared a nationwide lockdown, which was the first in Europe. France reported over 10,000 coronavirus cases on March 19th, and then, on March 23th, Boris Johnson announced a UK-wide partial lockdown. On March 26th, the United States officially became the country hardest hit by the pandemic  [9]. Table  1  summarizes the important events that happened during the first outbreak of the pandemic between January 2020 and March 2020. In this study, we mark and use these critical events in our analysis. Our work is underpinned by a large-scale dataset from Instagram. We started collecting the COVID-19 content on Instagram on 5 January 2020, and we continued this task until 30 May 2020.
C. ROADMAP & CONTRIBUTIONS In this study, we present and explore an Instagram dataset, covering several major keywords and hashtags related to COVID-19 during the first wave of the pandemic (Table  2 ). We hope that this study can help support a number of use cases. Summary of the main contributions in this study are: • We present a large dataset of COVID-19 related content published in Instagram including metrics such as posts content and reactions across various communities (Section III). • We perform a preliminary analysis of posts and their publishers with the goal of distinguishing published content by humans and machines (Section IV). We observe that 6. 9% of posts are distributed by bots. • We present interesting observations across hashtags by publishers (Section V). We observe many geographical hashtags are associated with '#quarantine.
A. COVID-19 DATASETS There have been a range of COVID-19 social media datasets released. To date, this predominantly covers textual data (e. g. Twitter). To assist in this, Kazemi et al. [13]  provides a toolbox for processing textual data related to COVID-19. In terms of data, the first efforts in this direction were from authors in  [2] , which provides a large Twitter dataset related to COVID-19 (by crawling major hashtags and trusted accounts). Another similar study  [3] , provides an Arabic Twitter dataset with a similar data collection methodology. Lopez et al. [4]  provide another Twitter dataset, including geolocated tweets. There are some further efforts on providing similar datasets from Twitter  [14] -  [16]. Melotte et al. [17]  present a more controlled and compact dataset without requiring extensive preprocessing or tweethydration.
The proposed dataset comprises tens of thousands of geotagged tweets originally collected over 255 days in 2020 across 10 metropolitan areas (in North America). Sharma et al. [5]  also made a public dashboard 2  available, summarising data across more than 5 million real-time tweets. In another study  [18] , to help the evaluation of the determinants and impact of the COVID-19 at a large scale, the authors present a new dataset with socio-demographic, economic, public policy, health, pollution and environmental factors for the European Union. Akindtande et al. [19]  present a dataset that investigates the magnitude of the misinformation content influencing scepticisms about the novel COVID-19 pandemic in Africa and the data is collected via an electronic questionnaire method from twenty-one Africa countries.
B. CONTENT ANALYSIS REVIEW These Twitter datasets have been used for various lines of analysis. For example, Saire and Navarro  [21]  use the data to show the epidemiological impact of COVID-19 on press publications. Singh et al. [22]  are also monitoring the flow of (mis)information flow across 2. 7M tweets, and correlating it with infection rates. They find that misinformation and myths are discussed, but at a lower volume than other conversations. To the best of our knowledge, the only paper that has covered Instagram is by Cinelli et al. [23] , who analyse Twitter, Instagram, YouTube, Reddit and Gab data about COVID-19. We complement this by making a public Instagram dataset available to the community. Kudchadkar et al. [24]  observed trends in concurrent #PedsICU and #COVID19 usage which reflect evolving information, knowledge gained, and collaborations among the global pediatric critical care community in Twitter.
The role of social media platforms in promoting COVID-19 conspiracy theories is also studied in  [28]. Pang et al. [29]  performed a comprehensive metaanalysis of publicly available global metabolomics datasets obtained from three countries (the United States, China and Brazil). They have implemented a computational pipeline to perform consistent raw spectra processing and conducted meta-analyses at pathway levels instead of individual feature levels. Mahmoudi et al  [30]  investigate the relationships between the counts of cases with COVID-19 and the deaths due to it in seven countries that are severely affected by this pandemic disease. In contrast to these prior works, we focus on image-based social media and explore the role that different account types play in disseminating COVID-19 related material.