dataesr/scientific-paragraphs-categorization huggingface dataset:
11. 002. P6556 Tween-20 Sigma Aldrich P9416 Water Sigma Aldrich W4502 103 Buffer Tango Thermo Fisher Scientific BY5 503 Denhardt's solution Thermo Fisher Scientific 750018 AccuPrime Pfx Polymerase (2. 5 U/ul) Thermo Fisher Scientific 12344032 ATP Thermo Fisher Scientific R0441 dNTP Mix Thermo Fisher Scientific R1121 Dyna MyOne Streptavidin C1 beads Thermo Fisher Scientific 65002 FastAP (1 U/mL) Thermo Fisher Scientific EF0651 GeneAmp 103 PCR Gold Buffer Thermo Fisher Scientific 4379874 Human Cot-I DNA Thermo Fisher Scientific 15279011 Klenow Fragment (10 U/mL) Thermo Fisher Scientific EP0052 Maxima Probe qPCR 2xMM Thermo Fisher Scientific K0233 Maxima SYBR Green kit Thermo Fisher Scientific K0251 Maxima SYBR Green kit Thermo Fisher Scientific K0253 Salmon sperm DNA Thermo Fisher Scientific 15632-011 SSC Buffer (203) Thermo Fisher Scientific AM9770 T4 DNA Ligase Thermo Fisher Scientific EL0012 T4 DNA Ligase, HC (30U/mL) Thermo Fisher Scientific EL0013 (Continued on next page) d RESOURCE AVAILABILITY B Lead contact B Materials availability B Data and code availability d EXPERIMENTAL MODEL AND SUBJECT DETAILS B Archaeology d METHOD DETAILS B Age at death estimation B Radiocarbon dating B Isotope analysis B DNA extraction and sequencing B Bioinformatics and quality control B Pathogen DNA scan d QUANTIFICATION AND STATISTICAL ANALYSES B Detecting genetic relatives B Investigating genetic ancestry using PCA B Admixture modeling with ADMIXTOOLS B Detecting admixture with qpWave B Inferring admixture models with qpAdm B Inferring admixture time with DATES B B Binomial simulations of founder allele counts B Detecting IBD and ROH segments in modern genomes B Demographic inference using IBD and ROH segments B Defining pathogenic founder variants B Imputation using pseudo-haploid ChromoPainter (PHCP) B Imputation using GLIMPSE.
Identification of the HDAC Genes and HAT Genes in Rose Nucleotide sequence of Arabidopsis members of the HDAC and HAT genes were retrieved from Arabidopsis genome database (https://www. arabidopsis. org/, accessed on 1 June 2020) using TBtools  [25]  using available gene ID (Table  S1 )  [8]. Arabidopsis HDACs and HATs amino acid sequences were used to identify HDACs and HATs putative homologues in the genomes of R. chinensis  [24] , of R. rugosa  [26] , of R. multiflora [27], and of Fragaria vesca  [28] , using blast Wrapper with an E-value ≤ 1 × 10 -5. Protein sequences of HDACs and HATs from R. chinensis, F. vesca, R. rugosa, R. multiflora were retrieved using NCBI blastp and Swiss-Prot database (BLAST: Basic Local Alignment Search Tool (nih. gov, accessed on 2 June 2020)). Conserved domains of HDAC and HAT proteins were detected by Batch CD-Search (https://www.
Conserved Sequence and Structure Model Analysis MEME (https://meme-suite. org/meme/tools/meme, accessed on 15 July 2021) was used to identify the conserved motifs in HDACs and HATs using the parameters of 20 motifs  [29]. The structure information of the HDACs and HATs was confirmed using R. chinensis genome annotation data  [24]. TBtools software v1. 098696 (Guangzhou, China)  [25]  was used to visualize the distribution of HDACs and HATs.
Chromosomal Location, Homologous Genes, and Synteny The genome sequence and genes annotation information of R. chinensis  [24] , R. Rugosa  [26]  and F. vesca  [28]  were downloaded. TBtools software  [25]  was used to visualize the distribution of the HDAC and HAT genes on the R. chinensis chromosomes. Blastp and Quick Run MCScanX Wrapper were performed using TBtools  [25]  to analyze tandem repeats and synteny of the HDAC and HAT genes between R. chinensis, R. rugosa, and F. vesca.
Transcriptome Analysis of HDACs and HATs Published RNA-seq data of R. chinensis 'Old Blush' (OB)  [30, 31] , R. wichurana (Rw) and one modern rose cultivar R. hybrida 'Yesterday' (Ry) (accession number: PRJNA436590) were downloaded from the NCBI (https://www. ncbi. nlm. nih. gov/, accessed on 5 February 2021) to monitor the expression patterns of the HDACs and HATs. Heat maps were drawn by TBtools  [25].
The Identification of HDACs and HATs HDAC and HAT gene members were retrieved in the six species (R. chinensis, R. rugosa, R. multiflora, F. vesca, A. thaliana, and Oryza sativa; Table  1 ) based on previous reports or by BLAST search. In F. vesca, alternative splicing transcript variants were identified for a number of HDAC and HAT genes, all of which retained the t1 suffix for further analyses. The number of HDAC and HAT genes varied among the six species (Table  1 ). Blastp and conserved domain search identified 23 HDACs in R. chinensis, 26 HDACs in R. multiflora, 12 HDACs in R. rugosa and 14 HDACs in F. vesca. Concerning the HAT genes, 10 were identified in R. chinensis, 15 in R. multiflora, 11 in R. rugosa, and 13 in F. vesca (Tables  S3-S6 ). They were named according to the naming conventions in Arabidopsis  [8].
Data Availability Statement: Genomic data of R. chinensis 'Old Blush' can be downloaded from https://lipm-browsers. toulouse. inra. fr//pub/RchiOBHm-V2/, accessed on 25 May 2020  [24]. Genomic data of R. rugosa can be downloaded from http://eplant. njau. edu. cn. Genomic datas of Rosa multiflora and Fragaria vesca can be downloaded from https://www. rosaceae. org/, accessed on 27 May 2020  [55]. Published RNA-seq data of R. chinensis 'Old Blush'(OB)  [30, 31] , R. wichurana (Rw),ratepe and R. hybrida 'Yesterday' (Ry) (accession number: PRJNA436590) can be downloaded from the NCBI (https://www. ncbi. nlm. nih. gov/, accessed on 5 February 2021).
Control and DDX42 KO cell populations were generated and pre-treated or not with IFN prior to infection with HIV-1 Renilla. Of note, CRISPR/ Cas9 KO of DDX42 induced only a partial decrease in DDX42 protein levels  (Fig 2A)  and cell populations tended to derive (not shown), suggesting a potential impact of DDX42 on cell proliferation or longterm survival. We observed however that DDX42 partial depletion with all 3 sgRNAs significantly improved HIV-1 infection, confirming that endogenous DDX42 had a negative impact on HIV-1 replication. Interestingly, the increase in infection efficiency induced by DDX42 KO was observed independently of the IFN treatment. DDX42 is not an ISG, as shown in several cell lines (U87-MG, T98G, HEK293T, Jurkat) and in primary T cells and monocyte-derived macrophages  (Fig EV1C and GSE46599; Goujon et al, 2013a).
DNAs from cells infected for 48 h in the presence of AZT and 3TC were used as a control. Mixed-effects analysis on log-transformed data with Dunnett's test. B Silencing efficiency in parallel samples from A. C Heat-annealed ODN/RNA 1-294 complex was incubated with HIV-1 RT and increasing concentrations of recombinant DDX42. Reverse transcription was initiated by addition of the four dNTPs. Extension was for 1, 5, 20 or 60 min and samples were analyzed by PAGE 8% (P/T: primer/template; SSDNA: Strong-Stop DNA). A representative autoradiograph is shown. D Quantification of 3 biological replicates performed as in C. Two-way ANOVA on log-transformed data with Dunnett's test. E PLAs were performed in MDMs infected with HIV-1 or not (N. I. CTRL), using anti-Capsid and anti-DDX42 antibodies (nuclei stained with Hoechst).
Images were acquired using a LSM880 Airyscan microscope. Left: representative images, scale-bar: 10 lm. Right: Average punctae quantified per cell in 3 biological replicates done on MDMs from different donors with mean AE SD (n > 65 cells per condition). Mann-Whitney test. F siRNA-transfected TZM-bl cells were infected with the indicated VSV-G-pseudotyped, replication competent viruses and b-galactosidase signals measured 24 h later. Two-way ANOVA on log-transformed data with Dunnett's test. G Silencing efficiency in parallel samples from (F). H siRNA-transfected U87-MG/CD4/CXCR4 cells were infected with HIV-1-HIV-2-FIV-EIAV-based, GFP-coding LVs and infection efficiency was scored 24 h later by measuring the percentage of GFP expressing cells by flow cytometry. Two-way ANOVA on log-transformed data with Dunnett's test.
18. 66. 3). Demultiplexing was performed using Illumina's conversion software (bcl2fastq 2. 20). The quality of the raw data was assessed using FastQC (v0. 11. 5) from the Babraham Institute and the Illumina software SAV (Sequencing Analysis Viewer). Potential contaminants were investigated with the FastQ Screen  (Wingett & Andrews, 2018) (v0. 11. 4 ) software from the Babraham Institute. Sequencing reads were trimmed using Cutadapt  (Martin, 2011) (v1. 13) , with options -g [primer sequence] -u [length of remaining 3 0 bases] -e 0. 2 -m 18 -l 20, to remove primer sequences and retrieve the 20 bases long sequences corresponding to sgRNAs. These retrieved sequences were then aligned to the GecKOv2 Human Library (A or B) reference sequences (keeping only non-duplicated sgRNA sequences, the duplicated ones being annotated) using Bowtie  (Langmead et al, 2009)  (v1.
For the RNA extraction and RT-qPCR analysis, specifically in Huh-7 cells, total RNAs were extracted from cell lysates using the NucleoSpin RNA II kit (Macherey-Nagel), following the manufacturer's protocol. Equal amounts of purified total RNA were used to synthetized first strand cDNA using random hexamers (Thermo Fisher) and the ReverAid H Minus Moloney murine leukemia virus (M-MuLV) reverse transcriptase (Thermo Fisher). Quantitative realtime PCR was performed on a real-time PCR system (Quant Studio 6 Flex from Applied Biosytems) with SYBR green PCR master mix (Life Technologies). Data were analyzed with the ΔΔCt method. All the samples were analyzed in technical triplicates and normalized to GAPDH (glyceraldehyde-3-phosphate dehydrogenase) as endogenous reference control. The primer sequences used for Fig EV4A  (Huh-7 ) were as follow: GAPDH Forward: 5 0 -GGTCGGAGTCAAC GGATTTG-3 0 , reverse: 5 0 -ACTCCACGACGTACTCAGCG-3 0 ; DDX42 Forward: 5 0 -GGCCTATACCCTACTCACTCCC-3 0 , reverse: 5 0 -CCACCA ATGTTCAGCTTTTTTCC-3 0.
0 anti-HIV-1 Capsid antibody (National Institutes of Health (NIH) AIDS Reagent Program #4121), or J2 anti-dsRNA antibody (SCICONS), or anti-SARS-CoV-2 Nucleoprotein (N; BioVision), and rabbit anti-DDX42 antibody (HPA023571, Sigma-Aldrich) diluted in NGB buffer or in Duolink â blocking solution for 1 h. After 2 washes in PBS1X, the cells were incubated with the DUOLINK â in situ PLA â Probe Anti-rabbit minus (DUO92006) and DUOLINK â in situ PLA â Probe Anti-mouse plus (DUO92001) for 1 h at 37°C. After 2 washes in PBS1X, the ligation mix was added for 30 min at 37°C. After 2 washes in PBS1X, the cells were incubated with the amplification mix for 100 min at 37°C followed by 3 washes in PBS1X. In the case of SARS-CoV-2 infection, an additional staining was performed by incubating cells in an anti-mouse Alexa Fluor secondary antibody.
Immunoblot analysis Cell pellets were lysed in sample buffer (10 mM Tris-HCl pH7. 6, 150 mM NaCl, 1% Triton X100, 1 mM EDTA, 0. 1% deoxycholate, 2% SDS, 5% Glycerol, 100 mM DTT, 0. 02% bromophenol blue), resolved by SDS-PAGE and analyzed by immunoblotting using primary antibodies specific for human DDX42 (HPA023571, Sigma-Aldrich; dilution 1/300), Flag (M2, Sigma-Aldrich; 1/1,000), MX1 (PA5-22101, ThermoFisher Scientific; 1/1,000), MX2 (HPA030235, Sigma-Aldrich; 1/1,000), IFITM3 (11714-1-AP, ProteinTech; 1/ 1,000), and Actin (A1978, Sigma-Aldrich; 1/5,000), followed by secondary horseradish peroxidase-conjugated anti-mouse or anti-rabbit immunoglobulin antibodies and chemiluminescence (Bio-Rad). Images were acquired on a ChemiDoc TM gel imaging system (Bio-Rad).
After 2 h, the viral inoculum was replaced with fresh DMEM 10% FBS 1% P/S. 24 h post-infection the cells were harvested and samples separated in half for Western blot and flow cytometry analysis. For the latter, cells were fixed and permeabilized using BD Cytofix/ Cytoperm (Fisher scientific) for 30 min on ice (all the following steps were performed on ice and centrifuged at 4°C) and then washed tree times with wash buffer. Cells infected with YFV, ZIKV and DENV-2 were incubated with the pan-flavivirus anti-Env 4G2 antibody for 1 h at 4°C and then with Alexa 488 anti-mouse IgG secondary antibodies (Thermo Fisher) for 45 min at 4°C in the dark. Data were acquired with an Attune NxT Acoustic Focusing Cytometer (Life technologies) and analyzed using FlowJo software.
Poly(I:C) and G4 pull-downs Biotinylated, high molecular weight poly(I:C) and 3 0 -Biotin-TEG labeled RNA oligonucleotides were ordered from InvivoGen and Eurofins, respectively; RNA oligonucleotide sequences were as follow: TRF2 G4 5 0 -CGGGAGGGCGGGGAGGGC-3 0 and Mutated (Mut. ) TRF2 G4 5 0 -CGUGAGUGCGCUGAGGGC-3 0 (Lavigne et al, 2021); and CHIKV G4 5 0 -GCAGGGCGUGCAAGGGGGCUGGGAGCAGGGGA ACGUGGUG. The latter is a sequence from CHIKV reference genome (GenBank accession number EU224268) predicted to potentially form G4s with G4Hunter algorithm (which allows to predict potentiel quadruplex forming sequences based on local guanine enrichment;  Bedrat et al, 2016) , using the MultiFastaSeeker Shiny application from  (Lacroix, 2019). The 3 0 -Biotin-TEG labeled oligonucleotides were folded in 10 mM Tris-HCl pH 7.
Analysis of high-throughput sequencing reads Sequenced reads were filtered by quality and sequence adaptors removed using fastp v0. 20. 1 (https://github. com/OpenGene/fastp; Chen et al, 2018) with following parameters "fastp --qualified_ quality_phred 20 --disable_length_filtering --detect_adapter_for_pe". Reads were pseudo-mapped against human cDNA sequenced downloaded from Gencode database (https://www. gencodegenes. org/) and transcripts abundance estimated with Kallisto v0. 46. 2 (https:// pachterlab. github. io/kallisto/about; Bray et al, 2016)  with parameters "--bias --bootstrap-samples 100".
Differential analysis with DESeq2 Differential expressed genes upon siRNA transfection were obtained using DESeq2  (Love et al, 2014; version 1. 32. 0)  in R (version 4. 1. 0). Briefly, transcript estimations were transformed in gene counts with tximport package  (Soneson et al, 2015)  and the differential expression obtained with the model design "~condition + replicate".
Pathway analysis For each set of siRNA, a pathway analysis was performed using clusterProfiler R's package  (Wu et al, 2021). Briefly, for each siRNA tested in each cell type, a pathways analysis was performed on differential upregulated or downregulated genes. Pathways enriched with an adj P-value < 0. 05 were kept for the downstream analyses. For each selected pathways, the fold-change was calculated as the median fold-change of the DEGs involved in each pathway and displayed as a heatmap for comparison. All scripts are available in the GitLab deposit: https://gitbio. ens-lyon. fr/LBMC/RMI2/siddx42-in-a549-and-u87-mg-cells.
Acknowledgements We wish to thank  Zhanna Santybayeva  (illustration4science. com)for the synopsis image,  Tom Doyle  and  Chad Swanson  for their useful comments on the manuscript, and  Matthieu Lewis ,  Nadine Laguette ,  Nathalie Arhel ,  Juliette Fernandez ,  Jean-Luc Battini ,  Georg Kochs ,  Andres Merits ,  Fr ed eric Tangy ,  Daniela Verga ,  Sylvie van de Werf  and  Nathan Sherer  for the generous provision of reagents, protocols and/or for helpful discussions. We are grateful to  Guillaume Bec  for the generous gift of HIV-1 RT and to  Nicolas Manel  and  Helena Izquierdo-Fernandez  for sharing their protocol for efficient knockdown in primary CD4 + T cell using CRISPR/Cas9 RNP electroporation. We are grateful to  Myriam Boyer ,  St ephanie Viala ,  Baptiste Monterroso  and  Orestis Faklaris  from the imaging and flow cytometry facility MRI, for advice with flow cytometry and confocal microscopy, respectively.
This work was supported by the  Institut National de la Sant e et de la Recherche M edicale (INSERM)  (to CG), the  ATIP-Avenir programme  (to CG), institutional funds from the  Centre National de la Recherche Scientifique (CNRS)  and  Montpellier University, Agence de Recherche ANRS France REcherche Nord&Sud Sida/HIV et H epatites  ( ECTZ21792  to CG;  ECTZ35478  to OM),  Sidaction  (to CG), a  Starting Grant  from the  European Research Council  ( ERC StG 759226, ANTIViR ) (to CG), PhD studentships from the  Ministry of Higher Education and Research  (to BB, JM and AR), and a 4 th year PhD studentship from the  Fondation pour la Recherche M edicale  (to BB,  FDT201904008024  and JM, FDT202106013175). SR and HP acknowledge financial support from the  France G enomique National  infrastructure, funded as part of " Investissement d'Avenir" program  managed by  Agence Nationale de la Recherche  ( ANR-10-INBS-09 ).
Data availability 1 RNA-Seq data at Gene Expression Omnibus GSE207288: https:// www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE207288. 2 The code used for RNA-seq analysis: https://gitbio. ens-lyon. fr/ LBMC/RMI2/siddx42-in-a549-and-u87-mg-cells. Expanded View for this article is available online.
Genomic datasets The Latin American individual samples analyzed here were part of  CANDELA Consortium (Ruiz-Linares et al. 2014). The CANDELA Consortium samples (http://www. ucl. ac. uk/silva/candela) have been described in detail in previous publications  (Ruiz Linares et al 2014; Chacon-Duque et al. , 2018). These data include a total of 6,630 volunteers from five Latin American countries  (Brazil, Chile, Colombia, Mexico and Peru). This dataset was genotyped on the Illumina HumanOmniExpress chip platform including 730,525 SNPs. We also collated reference populations from regions that have contributed to the admixture in Latin America. For Native American samples we used individuals previously genotyped by  Chacon-Duque et al. (2018). This dataset compromises 19 Native American populations from throughout the Americas with genotype data (supplementary table  S1 ).
(2018). After this filtering procedure, the five Latin American populations consisted of 190 Brazilians (BRA), 1125 Colombians (COL), 896 Chileans (CHL), 773 Mexicans (MEX) and 834 Peruvians (PER). From our Native American, European, and Sub-Saharan African reference populations, we also removed individuals that contained more than 1% of ancestry from another group based on the ADMIXTURE analysis described above. After this extra filter our final reference dataset was composed of 142 Native Americans, 205 Europeans, and 206 Sub-Saharan Africans.
Acknowledgements We thank the volunteers for their enthusiastic support for this research. We also thank  Alvaro Alvarado ,  Mónica Ballesteros Romero ,  Ricardo Cebrecos ,  Miguel Ángel Contreras Sieck ,  Francisco de Ávila Becerril ,  Joyce De la Piedra ,  María Teresa Del Solar ,  Paola Everardo Martínez ,  William Flores ,  Martha Granados Riveros ,  Rosilene Paim ,  Ricardo Gunski ,  Sergeant João Felisberto Menezes Cavalheiro ,  Major Eugênio Correa de Souza Junior ,  Wendy Hart ,  Ilich Jafet Moreno ,  Paola León-Mimila ,  Francisco Quispealaya ,  Diana Rogel Diaz ,  Ruth Rojas , and  Vanessa Sarabia , for assistance with volunteer recruitment, sample processing and data entry. We also thank  Francois Balloux ,  Aida Andres ,  Mark McCarthy , and  Etienne Patin  for helpful discussion and critical comments on earlier versions of the manuscript.
1. (Lonsdale et al. 20138ed on the UK BioBank cohort available at: http://www. nealelab. is/ukbiobank. For our eQTL query, we retrieved cis-associations summary statistics of 15 human immune cell types from the DICE (Database of Immune Cell Expression, Expression quantitative trait loci [eQTLs] and Epigenomics) project(Schmiedel et al. 2018), available at: https://dicedatabase. org/downloads. We also retrieved cis-association summary statistics from adipose (subcutaneous, and visceral omentum), muscle (skeletal), and liver tissue from the GTEx Project v7(Lonsdale et al. 2013) available at: https://gtexportal. org/home/datasets.
Results Genetic and clinical features of patients with pRD. The pRD cohort included a 5-month-old asymptomatic male (P1)  21  and 15 patients with a CID or CID-G/AI phenotype (P2-16) (Supplementary Table  1 ). Eleven patients carried RAG1 (7 compound heterozygous and 4 homozygous) and 5 patients carried RAG2 (3 compound heterozygous and 2 homozygous) variants, for a total of 16 RAG1 and 8 RAG2 distinct mutant alleles (Extended Data Fig. 1a ). Pathogenicity was assigned based on combined assessment of in vitro recombination activity assays  2, 3  and curated data obtained from ClinVar database and/or analysis following guidelines of American College of Medical Genetics and Genomics  23  and the Association for Molecular Pathology (Extended Data Fig. 1b  and Supplementary Table  2 ). All patients displayed profound lymphopenia as compared to aged-matched healthy ranges  24  (Extended Data Fig.
5e ). The symmetric structure of the expanded lineages with radial projections of the diversified sister  By assessing inflammatory and T FH cell-specific cytokineproducing capacity of T H cells in vitro we found elevated IFN-γ, TNF-α and IL-21 production in the patients (Fig. 8g, h ). To determine whether increased cytokine production of total T H cells merely results from the abundant presence of effector cells (cT FH and memory) in pRD or whether these cells indeed possess elevated cytokine-producing capacity, we measured cytokine production on presorted T H populations. Notably, IFN-γ and TNF-α production of effector T H cells (cT FH and memory) was comparable to those of HCs (Fig. 8i, j ). Elevated IFN-γ and TNF-α production of naive T H cells observed in pRD may reflect the inclusion of confirmed a significant decrease in the proportion of naive cells and revealed the strong expansion of EM T H cells in pRD (Extended Data Fig.
When appropriate, surface-stained cells were fixed, permeabilized and followed by intracellular staining. Cells were analyzed and/or sorted with a FACSAria II (BD Bioscience) using FACSDiva software v. 10. Fluorescence-activated cell sorting data were visualized with FlowJo software v. 10. 7. 1 (TreeStar) and, when indicated, further analyzed by FlowSOM (v. 2. 5), DownSample (v. 3. 0), t-SNE (v. 2. 0) and HyperFinder (v. 0. 6. 2) FlowJo plugins. Unsupervised high-dimensional clustering for B cell subset identification and quantification. Unsupervised high-dimensional analysis was performed using FlowSOM plugin  60  to achieve objective B cell subset identification and quantification from the patients with pRD. Briefly, flow cytometry data obtained from four patients (P9, P12, P14 and P16) and four age-appropriate HCs (C1, C3, C5 and C7) (inclusion criteria of data were having at least 5,000 live CD19 + cells and acquisition performed with identical flow cytometry panel) for CD21, CD24, CD27, CD38, IgD and IgM parameters were simultaneously used.
First, full datasets were randomly downsampled to 5,000 live CD19 + cells from each donor using DownSample FlowJo plugin. Next donors were concatenated and then each cell of the generated B cell pool was assigned by a self-organizing map in a 10 × 10 grid, resulting 100 different nodes. Then, minimal spanning trees (MSPs) were built from the nodes in each donor to visualize similar nodes in branches and their relative connection to each other (Extended Data Fig. 3a ). To identify B cell populations, nodes were classified into metaclusters that revealed 16 different metaclusters each representing a unique B cell subset (Extended Data Fig. 3b ). To associate these metaclusters to B cell subsets, first, the mean values of each marker (Extended Data Fig. 3c ) were min-max normalized in each metacluster for all donors to rescale value range of 0-1.