dataesr/scientific-paragraphs-categorization huggingface dataset:
Regional and country-specific NextStrain builds To investigate more-granular changes in lineage dynamics within a specific country or region in Africa, we used the NextStrain pipeline (https://github. com/nextstrain/ncov) to generate the regional and country-specific builds for African countries  (75). First, all sequence data and metadata were retrieved from the GISAID sequence database and filtered for Africa based on the "region" tab for inclusion in regional and country-specific African builds. For country-specific builds, ~4000 sequences from a given country were randomly selected and analyzed against ~1000 randomly selected sequences from the Africa "nextregions" records that do not match the focal country of interest. For regional (e. g. , West Africa) builds, ~4000 sequences from the focal region were selected at random and analyzed against ~1000 randomly selected sequences from the Africa "nextregions" records that do not match the focal region of interest.
The methodological pipeline for NextStrain is well documented and performs all analyses within one workflow, including filtering of sequences, alignment, tree inference, molecular clock, and ancestralstate reconstruction. For more information, please visit https://docs. nextstrain. org/en/ latest/index. html. All regional and country-specific builds are regularly updated to keep track of the evolving pandemic on the continent. All builds are publicly available under the links provided in tables S1 and S2 as well as on the NextStrain web page (https://nextstrain. org/sars-cov-2/ #datasets).
ACKNOWLEDGMENTS First and foremost, we acknowledge authors in institutions in Africa and beyond who have made invaluable contributions toward specimen collection and sequencing to produce and share, via GISAID, SARS-CoV-2 genomic data. We also acknowledge the authors from the originating and submitting laboratories worldwide who generated and shared SARS-CoV-2 sequence data, via GISAID, from other regions in the world, which was used to contextualize the African genomic data. A full list of GISAID sequence IDs used in the current study is available in table S4. Funding: Sequencing efforts in the African Union Member States were supported by the  Africa Centers for Disease Control (Africa CDC)-Africa Pathogen Genomics Initiative (Africa PGI)  and the  World Health Organization Regional Office for Africa (WHO AFRO)  through the transfer of laboratory infrastructure, the provision of reagents, and training.
The Africa PGI is supported by the  African Union ,  US Centers for Disease Control and Prevention (CDC) ,  Bill & Melinda Gates Foundation ,  Illumina Inc. ,  Oxford Nanopore Technologies , and other partners. In addition, all  Institut Pasteur  organizations and  CERMES  in Niger are part of the  PEPAIR COVID-19-Africa  project, which is funded by the  French Ministry for European and Foreign Affairs. The  KwaZulu-Natal Research Innovation and Sequencing Platform (KRISP)  and  Centre for Epidemic Response and Innovation (CERI)  are supported in part by grants from  WHO , the  Rockefeller Foundation  ( HTH 017 ), the  Abbott Pandemic Defense Coalition (APDC) , the  US National Institutes of Health (NIH)  ( U01 AI151698 ) for the  United World Antivirus Research Network (UWARN)  and the  INFORM Africa  project through the  Institute of Human Virology, Nigeria (IHVN)  ( U54 TW012041 ),  H3BioNet Africa  (grant no.
5D43TW009610). H3ABioNet is an initiative of the Human Health and Heredity in Africa Consortium (H3Africa) program of the African Academy of Science (AAS) and the US Department of Health and Human Services (HHS), NIH, and NIAID (5K24AI131928-04; 5K24AI131924-04); SANTHE is a DELTAS Africa Initiative (grant no. DEL-15-006). The DELTAS Africa Initiative is an independent funding scheme of the AAS's Alliance for Accelerating Excellence in Science in Africa (AESA) and is supported by the New Partnership for Africa's Development Planning and Coordinating Agency (NEPADAgency) with funding from the Wellcome Trust (grant #107752/Z/15/Z) and the UK government. From Brazil, J. S. X. was funded by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior-Brazil (CAPES)-Finance Code 001. Sequencing efforts from Côte d'Ivoire were funded by the Robert Koch Institute and the German Federal Ministry of Education and Research (BMBF).
Sequencing efforts in the Democratic Republic of the Congo were funded by the Bill & Melinda Gates Foundation under grant INV-018030 awarded to C. B. P. and further supported by funding from the Africa CDC through ASLM for Accelerating SARS-CoV-2 Genomic Surveillance in Africa, the CDC, US Army Medical Research Institute of Infectious Diseases (USAMRIID), Institut de Recherche pour le Développement (IRD)/Montepellier, University of California-Los Angeles (UCLA), and SACIDS FIND. Efforts from Egypt were funded by the Egyptian Ministry of Health, the Egyptian Academy for Scientific Research and Technology (ASRT) JESOR project #3046 (Center for Genome and Microbiome Research), the Cairo University anti-COVID-19 fund, and the Science and Technology Development Fund (STDF), project ID 41907. The sequencing effort in Equatorial Guinea was supported by a publicprivate partnership, the Bioko Island Malaria Elimination Project, which is composed of the government of Equatorial Guinea Ministries of Mines and Hydrocarbons, and Health and Social Welfare, Marathon EG Production Limited, Noble Energy, Atlantic Methanol Production Company, and EG LNG.
Analysis for the Gabon strains was supported by the Science and Technology Research Partnership for Sustainable Development (SATREPS), Japan International Cooperation Agency (JICA), and Japan Agency for Medical Research and Development (AMED) (grant number JP21jm0110013) and a grant from AMED (grant number JP21wm0225003). The Centre Interdisciplinaires de Recherches Medicales de Franceville (CIRMF) (Gabon) is funded by the Gabonese Government and TOTAL Energy inc. CIRMF is a member of the Central Africa Network on Tuberculosis, HIV/AIDS and Malaria (CANTAM), which is supported by the European and Developing Countries Clinical Trials Partnership (EDCTP). The work at the West African Centre for Cell Biology of Infectious Pathogens (WACCBIP) (Ghana) was funded by a grant from the Rockefeller Foundation (2021 HTH 006), an IRD grant (ARIACOV), an African Research Universities Alliance (ARUA) Vaccine Development Hubs grant with funds from Open Society Foundation, National Institute of Health Research (NIHR) (17.
Liberian efforts were funded by the Africa CDC through a subaward from the Bill & Melinda Gates Foundation, and efforts from Madagascar were funded by the French Ministry for Europe and Foreign Affairs through the REPAIR COVID-19-Africa project coordinated by the Pasteur International Network association. Sequencing from Malawi was supported by the Wellcome Trust. Contributions from Mali were supported by Fogarty International Center and NIAID sections of the NIH under Leidos-15X051, award numbers U2RTW010673 for the West African Center of Excellence for Global Health Bioinformatics Research Training and U19AI089696 and U19AI129387 for the West Africa International Center of Excellence for Malaria Research. Funding for surveillance, sampling, and testing in Madagascar was provided by the WHO, the CDC (grant no.
Rwanda's contributions were made possible by funding from the African Network for Improved Diagnostics, Epidemiology and Management of Common Infectious Agents (ANDEMIA), which was granted by the German Federal Ministry of Education and Research (BMBF grants 01KA1606, 01KA2021, and 01KA2110B) and the NIHR Global Health Research program (16/136/ 33) using UK aid from the UK Government. In addition to the South African institutions listed above, the University of Cape Town's work was supported by the Wellcome Trust (grant no. 203135/Z/16/Z), EDCTP RADIATES (RIA2020EF-3030), the South African Department of Science and Innovation (SA DSI), and SAMRC; Stellenbosch University's contributions were supported by SAMRC; and the University of Pretoria's contributions were funded by the G7 Global Health Fund and a BMBF ANDEMIA grant.
3A ). We then performed RNA-seq followed by differential expression analysis for the untreated and glucosamine-treated SMC3-3HA-glmS and WT parasites, which confirmed a significant knockdown of SMC3 at the transcript level in the SMC3-3HA-glmS parasites: 55% (q = 8. 5 x 10 -3 ) at 12 hpi and 69% (q = 1. 3 x 10 -39 ) at 24 hpi (Tables  6  and 7 , Fig. 3B ). To remove potential artifacts of glucosamine treatment, genes that were significantly up-or downregulated in the glucosaminetreated WT parasites at 12 and 24 hpi (Tables  8  and 9 ) were filtered out of the datasets for significantly up-and downregulated genes in the SMC-3HA-glmS parasites at the corresponding time points (Supp. Fig. 2 ). After this filtering, 104 and 932 genes were significantly downregulated at 12 and 24 hpi, respectively (Tables  10  and 11 , Fig.
, 2021; Oh et al. , 2021). In P. falciparum, the current genome-wide chromosome conformation capture (Hi-C) datasets do not provide evidence of typical enhancer-promoter interactions found in other eukaryotes  (Ay et al. , 2014; Bunnik et al. , 2019). Moreover, in S. cerevisiae, a cohesin mutant resulted in the de-repression of genes located in subtelomeric regions, perhaps via disruption of local chromatin structure  (Kothiwal & Laloraya, 2019). However, the invasion-related genes affected by PfSMC3 are scattered across the genome (Table  14 ). One possibility is that cohesin binding to the promoter of a gene merely inhibits the transcriptional machinery from assembling. In light of the ability of cohesin to entrap multiple DNA molecules, another intriguing possibility is that it tethers invasion-related genes together in a cluster that renders their promoters inaccessible to specific activating factors until the appropriate time of transcription.
The mass spectrometer was operated in a data-dependent mode. The parameters for the full scan MS were as follows: resolution of 60,000 across 350-1,500 m/z, AGC 1e 5 (as in  (Kensche et al. , 2016 )), and maximum injection time (IT) 150 ms. The full MS scan was followed by MS/MS for the top 15 precursor ions in each cycle with an NCE of 30 and dynamic exclusion of 30 s and maximum IT of 96 ms. Raw mass spectral data files (. raw) were searched using Proteome Discoverer 2. 3. 0. 523 (Thermo Fisher) with the SEQUEST search engine. The search parameters were as follows: 10 ppm mass tolerance for precursor ions; 0. 8 Da fragment ion mass tolerance; two missed cleavages of trypsin; fixed modification was carbamidomethylation of cysteine; and variable modifications were methionine oxidation, CAMthiopropanoyl on lysine or protein N-terminal, and serine, threonine, and tyrosine phosphorylation.
Zhang et al. , 2008). Obtained peaks with q-value cutoff 0. 05 for each time point were visualized in Integrative Genomics Viewer  (Robinson et al. , 2011)  along with ChIP-Input ratio coverage obtained from deeptool's bamCompare command  (Ramírez et al. , 2016). To map SMC3 binding to nearby protein coding genes, peak summits were extended 150 bp upstream and downstream, and bedtools closest command  (Quinlan & Hall, 2010)  were used with P. falciparum reference genome feature file (gff) (plasmoDB. org, version 3, release 56). Only regions 500 bp upstream or downstream near to the protein coding genes were considered further for downstream analysis. Centromeric regions (from  (Hoeijmakers et al. , 2012)  were corrected for changes in genome annotation. These regions were overlapped with SMC3 peaks dataset using bedtools intersect command  (Quinlan & Hall, 2010).
Library preparation was performed with the NEBNext® Ultra™ II Directional RNA Library Prep Kit for Illumina® (New England Biolabs E7760S) and paired end sequencing was performed on the Nextseq 550 platform (Illumina). Sequenced reads (150 bp paired end) were mapped to the P. falciparum genome  (Gardner et al. , 2002)  (plasmoDB. org, version 3, release 55) using "bwa mem"  (Li & Durbin, 2009) , allowing a read to align only once to the reference genome (option "-c 1"). Alignments were subsequently filtered for duplicates and a mapping quality ≥ 20 using samtools  (Li et al. , 2009). Gene counts were quantified with htseq-count  (Anders et al. , 2015) , and differentially expressed genes were identified in R (R Core Team, 2021) using package DESeq2  (Love et al. , 2014).
A 1:10 dilution of the quenched iRBC suspension was incubated with Sybr Green I (Sigma S9430) to stain the parasite nuclei. Quantification of the iRBCs was performed in a CytoFLEX S cytometer (Beckman Coulter) and analysis with FlowJoä Software. (DOI: 10. 6019/PXD035225, Username: reviewer_pxd035225@ebi. ac. uk, Password: agXjJYP4) FIGURE LEGENDS Figure  1. SMC3 is expressed across the IDC and localizes to HP1-independent nuclear foci (A) Cohesin complex subunits annotated in P. falciparum  (Gardner et al. , 2002). Image prepared with BioRender. com. (B) Schematic of P. falciparum intraerythrocytic developmental cycle (IDC). Yellow, approximate timing of DNA replication; Grey, approximate duration of schizogony (modified from  Ganter et al. , 2017 and Matthews et al. , 2018). Time points in this study -12 hpi (ring), 24 hpi (trophozoite), and 36 hpi (schizont) -are indicated.
and 36 (green) hpi. Closest unique protein coding genes to the extended SMC3-3HA peak summit (+/-500 bp) at 12, 24, and 36 hpi are shown in Table  4. (B) RNA-seq of an SMC3-3HA-glmS clone shows smc3 transcript levels (FPKM) at 12 (q = 8. 5 x 10 - 3 ) and 24 (q = 1. 3 x 10 -39 ) hpi in the absence (circles) or presence (squares) of glucosamine (GlcN). P-values are calculated with a Wald test for significance of coefficients in a negative binomial generalized linear model as implemented in DESeq2  (Love et al. , 2014). q = Bonferroni corrected P-value. Corresponding data can be found in Tables  6  and 7. (C) and (D) MA plots of log2(glucosamine-treated/untreated, M) plotted over the mean abundance of each gene (A) at 12 hpi (C) and 24 hpi (D). Transcripts that were significantly higher (above x-axis) or lower (below x-axis) in abundance in the presence of glucosamine are highlighted in red (q ≤ 0.
1). smc3 is highlighted in green. Three replicates were used for untreated and glucosamine-treated parasites, with the exception of the untreated 12 hpi parasites, for which there were two replicates. P-values were calculated with a Wald test for significance of coefficients in a negative binomial generalized linear model as implemented in DESeq2  (Love et al. , 2014). q = Bonferroni corrected P-value. (C) RNA-seq of an SMC3-3HA-glmS clone shows transcript levels (FPKM) for rap2 (PF3D7_0501600) at 12 (q = 3 x 10 -2 ) and 24 (q = 3. 3 x 10 -2 ) hpi and gap45 (PF3D7_1222700) at 12 (q = 8 x 10 -1 ) and 24 (q = 1. 6 x 10 -2 ) hpi in the absence (black) or presence (grey) of glucosamine (GlcN). P-values are calculated with a Wald test for significance of coefficients in a negative binomial generalized linear model as implemented in DESeq2  (Love et al.
ACKNOWLEDGEMENTS This work was supported by the  Agence Nationale de la Recherche  (grant  ANR-21-CE15-0010 PlasmoVarOrg  to JMB and grant  ANR-11-LABEX-0024-01 ParaFrap  to ASc); the  European Research Council  (grant PlasmoSilencing  670301  to ASc and grant  PlasmoEpiRNA 947819  to SB); the  Academic Research Fund  (Tier 2) of the  Ministry of Education, Singapore  ( MOE2018-T2-2-131  to PRP); and the  Merlion Project  ( 6. 11. 18  to PRP). Work in the laboratories of PRP and PCD was funded by the  National Research Foundation of Singapore  through the  Singapore-MIT Alliance for Research and Technology Antimicrobial Resistance Interdisciplinary Research Group. JMB was supported by a  European Molecular Biology Organization long-term postdoctoral fellowship  ( EMBO ALTF 180-2015 ), the  Institut Pasteur Roux-Cantarini postdoctoral fellowship , and a  ParaFrap  fellowship.
Although various vaccines are now commercially available, they have not been able to stop the spread of COVID-19 infection completely. An excellent strategy to get safe, effective, and affordable COVID-19 treatments quickly is to repurpose drugs that are already approved for other diseases. The process of developing an accurate and standardized drug repurposing dataset requires considerable resources and expertise due to numerous commercially available drugs that could be potentially used to address the SARS-CoV-2 infection. To address this bottleneck, we created the CoviRx. org platform. CoviRx is a user-friendly interface that allows analysis and filtering of large quantities of data, which is onerous to curate manually for COVID-19 drug repurposing. Through CoviRx, the curated data have been made open source to help combat the ongoing pandemic and encourage users to submit their findings on the drugs they have evaluated, in a uniform format that can be validated and checked for integrity by authenticated volunteers.
With the use of various drug databases it is feasible to determine the biological function of a protein computationally and validate the process statistically. This technique, however, comes with its own set of challenges. One such challenge is data availability. Public access to high-quality, valuable information such as clinical trials data is still limited. Although there are a few open-access databases, such as the Drug Repurposing Hub  [6] , which provide drug repurposing candidates, very few are specific to COVID-19, such as Excelra  [7]  and NCATS  [8]. Additionally, the significant sizes of these databases often create computational bottlenecks. Through this study we explored the problems of data accessibility, availability, and interpretability by developing CoviRx, a robust web-based application.
This computer science paper should be read in conjunction with research by MacRaild et al. , 2022  [10] , which describes the pharmacological methodology for systematic down-selection of repurposed drug candidates for COVID-19. To increase collaboration with the other researchers, features have been incorporated in CoviRx that allow registered users to submit their findings on any drug candidate to the CoviRx web app. Following a thorough peer review process, these submissions will be merged with the primary database, which we herein report. The user can download the available data as JSON (JavaScript Object Notation) or PDF (Portable Document Format) file, and unique URLs. This provides an elegant solution to the problem of data non-standardization and lack of data mining, interpretation, and manipulation-friendly datasets  [11].
Numerous graphical representations  [16]  are present throughout the web app to give the users an overview of the dataset and enhance their experience. Its modular architecture ensures that it is flexible enough to be extended to any other disease without disrupting its underlying functionality. CoviRx thus is a one-stop application for accessing high-quality data related to COVID-19 drug repurposing and, in the future, any other epidemic. CoviRx can be accessed at www. covirx. org.
Data Description Drug data (with assays) were manually curated by our team from various research journals and publicly available drug databases. Regulatory approval data were obtained from the U. S. Food and Drug Administration (FDA)  [17] , Therapeutic Drug Administration, Australia (TGA)  [18] , and Inxight Drugs websites [19], while clinical trials data were acquired from clinicaltrials. gov [20] for 7817 drugs. These drugs were then passed through a series of 11 filters, and the resultant drugs were screened for possible action against COVID-19 to narrow down to twelve drugs for further studies on COVID-19 drug repurposing  [21]. Manual curation of data and datasets requires considerable time, labor, and financial resources. Therefore, to economise these resources, it was decided to open source this dataset and make it available with a user-friendly graphical interface so that other researchers working on their own filters for down-selecting drugs can use it as shown in Figure  1.
The CoviRx website consists of various identifiers such as synonyms, Chemical Abstract Service (CAS) number, and PubChem ID, ChEMBL ID, amongst others, which are hyperlinked to provide a seamless transition across different databases. Various druglikeness properties such as molecular weight, Partition Coefficient (logP), hydrogen bond donors (HBD) and hydrogen bond acceptors (HBA) are also included for each compound to assist with early drug discovery programs. A drug's original indication, mechanism of action (MOA), relevant pathways it interferes with, and associated targets are also included to provide a brief background about its original application. CoviRx provides users with SARS-CoV-2 drug repurposing data from several COVID-19 drug repurposing studies and the status of such drugs in COVID-19 trials.
The data types of each field and the type of constraints en- The CoviRx website consists of various identifiers such as synonyms, Chemical Abstract Service (CAS) number, and PubChem ID, ChEMBL ID, amongst others, which are hyperlinked to provide a seamless transition across different databases. Various drug-likeness properties such as molecular weight, Partition Coefficient (logP), hydrogen bond donors (HBD) and hydrogen bond acceptors (HBA) are also included for each compound to assist with early drug discovery programs. A drug's original indication, mechanism of action (MOA), relevant pathways it interferes with, and associated targets are also included to provide a brief background about its original application. CoviRx provides users with SARS-CoV-2 drug repurposing data from several COVID-19 drug repurposing studies and the status of such drugs in COVID-19 trials.
Tech Stack and Design Principles Modern technologies were employed to develop the front-end and back-end of the web app. Figure  4  demonstrates the interaction between different components like front-end and back-end servers, and the frameworks we have used to develop these. These frameworks are open-source, well documented, and highly flexible to use. Back-end servers, such as Apache  [22]  and cloud hosting platforms including Azure or AWS, were preferred over other alternatives as these provided support for both vertical and horizontal scalability. Currently, SQLite  [23] , a relational database management system, is being used to process website data; however, it will be migrated to PostgreSQL soon  [24, 25]  as it offers better concurrency control  [26]. Google OAuth  [27]  (login/logout), Google reCAPTCHA [28], and Google simple mail transfer protocol (SMTP)  [29]  have been used during development.
CoviRx sends emails to its users for various purposes, such as a copy of responses filled by them in contact and "contribute drug" forms; an invitation to the admin panel; and reporting errors that occur during drug upload. A python library called Premailer was used because emails do not support CSS or JS files, and it is important to deliver well-styled HTML emails to our users  [30]. Few properties of database systems and websites  [31] [32] [33]  form the foundation for CoviRx's architecture. These design principles have been discussed in detail in Table  1. frameworks are open-source, well documented, and highly flexible to use. Back-end servers, such as Apache [22] and cloud hosting platforms including Azure or AWS, were preferred over other alternatives as these provided support for both vertical and horizontal scalability.