dataesr/scientific-paragraphs-categorization huggingface dataset:
Concluding remarks Caveats of this study are that only the TCRβ chain was assessed, whereas true determination of specificity can only be achieved with paired TCR α/β chains. However, as the analysis yielded significant results in the anti-SARS-CoV-2 response of COVID-19 patients and in vaccinated HD, in the anti-CMV response in CMV seropositive individuals, and the finding pertaining to EBV and MS could be shown in four independent cohorts, we are confident that these results would only get stronger in a paired analysis. Additionally, all 528 database-derived, multimerconfirmed EBV-specific sequences were MHC-I restricted. Therefore, B cell help by CD4 + T cells especially in the context of the MS risk allele DRB1*15 could not be evaluated. Lastly, quantifying EBV-specific TCRβ sequences does not allow for a judgment on the quality of the T cell response to EBV.
TCR repertoire analysis TCRβ repertoires were queried against public databases (download in April 2022) of HLA-restricted TCRβ rearrangements specific for four common pathogens from the VDJdb (SARS-CoV-2, EBV, CMV, and influenza A virus; database confidence score >0;  Bagaev et al. , 2020)  plus SARS-CoV-2-specific rearrangements from the IEDB  (Vita et al. , 2019)  in order to have a comparable number of sequences in each category (644 SARS-CoV-2, 528 EBV, 840 CMV, 381 influenza A). Database TCRβ sequences with a CDR3 amino acid length smaller than six, or with missing information (organ, protein, epitope, HLA restriction, variable β chain family) were excluded (Table  S1 ). HLA-A*02-positive individuals were preselected due to the known reduced allelic frequency of HLA-A*02 in MS patients  (International Multiple Sclerosis Genetics consortium et al.
, 2011)  and to maximize detection power, because most published sequences in the database were restricted to this allele. Preselection for the MS risk allele HLA-DRB1*15 was not necessary, as there were no sequences for the four chosen pathogens restricted to this allele. Twins were not preselected, as there is no HLA bias in a paired monozygotic twin sibling analysis. Subsequently, all TCR sequences were collected for each individual from the databases restricted to the individual's HLA alleles and matched using the TCR Vβ family and the amino acid sequence of the CDR3. Matches were then aggregated according to the pathogen. In case of the epitope subanalysis, only TCR sequences for a specific epitope were matched in individuals positive for the HLA allele for which the epitopespecific multimer was restricted.
scRNAseq analysis The following datasets were used for the scRNAseq analysis: four HD, where cells were labelled with 44 different epitope/HLA specific dextramers and then sorted and subsequently sequenced using 10x Genomics protocols  (Boutet et al. , 2019 ; Zenodo accession number 6952657), as well as CSF data from six HD and five MS patients from  Pappalardo et al. (2020). The data from  Pappalardo et al. (2020)  were financially supported by the National Institutes of Health and downloaded from the National Institutes of Health's dbGaP database (accession phs002222. v2. p1). Data were combined into Seurat objects, SCtransformed, and mapped according to the level 1, 2, and 3 granularities provided by the Seurat package  (Hao et al. , 2021). Cells were then subset to CD8 + T cells (level 1) and the CD8 + T cell subclusters of level 2, before the level 3-granularity cluster distribution was analyzed.
Statistical analysis Significance was assessed using linear models for the crosssectional analysis and linear mixed models for the twin and validation cohort. The former included the covariates sequencing depth, age, sex, and disease status (MS, yes/no; and COVID-19, yes/no), the latter included sequencing depth, age, MS status, and twinship  (Gerdes et al. , 2020). In case of the MS twin study, there was no need to adjust for sex, but as the blood samples were not always drawn on the same day, the model had to include age. Additionally, the MS twin study models included information about symptomatic EBV infection in childhood/ infectious mononucleosis. HLA information for 16 restriction alleles from the TCRβ sequence databases was added to the models of the discovery and MS twin cohort, but not the validation cohort, due to the lower number of patients.
False discovery rate adjustment of model P values for multiple comparisons with the number of assessed pathogens (4) resulted in the displayed q values  (Bonferroni, 1936). The immunoPETE samples were analyzed in a nested model (pools within samples within individuals) and adjusted for sequencing depth (number of input α β T cells), age, sex, MS, vaccination, and anti-VLA-4/ anti-CD20 treatment. The epitope subanalysis models did not include the HLA information, as the donors and sequences were all matched/restricted to one epitope-specific HLA allele. The lasso regression models to evaluate MS association of EBVspecific sequences in the HLA-A*02-positive individuals of the discovery cohort were performed using the glmnet R package  (Simon et al. , 2011)  with the factor MS as target variable, the individuals' HLA alleles as the covariates, an α value of 0 and 3 folds.
Online supplemental material Table S1 lists the pathogen-specific TCRβ sequences from the databases VDJdb and IEDB queried for this study, including organism, protein, epitope, and HLA information, as well as the sequence matches of the discovery and MS twin cohort for each individual. Table  S2  details the numbers of EBV-specific TCR sequences found in HLA classifier patterns. Table  S3  details the model parameters of the HLA-restricted, EBV epitope-specific subanalyses of the discovery, and MS twin cohort, including sequence and donor n numbers, estimates, errors, and P values. Disclosures: R. Gittelman reported personal fees from Adaptive Biotechnologies during the conduct of the study. F. Rubelt reported a patent to US10731212B2 issued and a patent to im-munoPETE related pending; and is an employee of Roche Diagnostics and receives salary, stock, and options as part of his employment compensation.
Acknowledgments The authors would like to thank the donors, without whom this study would not have been possible. We also acknowledge the technical assistance of  Barbara Meyring  and  Petra Kotte  and would like to thank  Thomas Snyder  for helpful discussions. This study was funded by the  Deutsche Forschungsgemeinschaft  grant  445569437  to  N. Schwab  and  H. Wiendl , grant  CRC128  Project  B01  to  N. Schwab , Project  A09  to  C. C. Gross  and  H. Wiendl , and  Z02  to  L. Klotz. Sequencing  of the MS twin cohort was supported by  Biogen. Sequencing analysis of the validation cohort was supported by  Roche. The  MS Twin study  was funded by the  Deutsche Forschungsgemeinschaft under Germany's Excellence Strategy  within the framework of the  Munich Cluster for Systems Neurology  ( EXC 2145 SyNergy: ID 390857198 ), the  Gemeinnützige Hertie Stiftung, Bavarian Association and National Association of the German MS Society ,  Dr.
12 , Supplementary Table  7 ). In order to further alleviate this issue, we compared the aggregated risk groups using CoxPH as well which showed similar HR pro les with the training dataset albeit with decreased statistical signi cance. This is due to the limitation in suboptimal aggregation of MCs (Supplementary Tables 9). In addition to survival, MCs also demonstrated distinct clinical differences. For instance, patients in MC1, MC11, and MC13 had signi cantly lower platelet counts (87, 48, and 76, respectively, p-value < 0. 001) compared to other clusters. We also detected discrete survival differences in patients (N = 2863) treated with hypomethylating agents (HMAs) and/or allogeneic hematopoietic stem cell transplant. Even after accounting for the different treatments, the risk groups continued to show signi cant differences in OS using semi-parametric Cox-PH model, where nonparametric alternative random-forest survival model showed improved separation in untreated cohort compared to treated samples as well (Supplementary Figs.
Acknowledgments We thank our sources of funding: the  HENRY & MARILYN TAUB FOUNDATION  (J. P. M. ), grants  R01HL118281  (to J. P. M. ),  R01HL123904  (to J. P. M. , R. A. P. ),  R01HL132071  (to J. P. M. , R. A. P. ),  R35HL135795  (all to J. P. M),  AA&MDSIF  (to V. V. , S. P. , J. P. M),  VeloSano Pilot Award ,  Vera and Joseph Dresner Foundation-MDS  (to V. V. ). C. G. was supported by a grant from the  Edward P. Evans Foundation. This work used the  High-Performance Computing Resource in the Core Facility for Advanced Research Computing at Case Western Reserve University.
Data sharing. Requests for additional information not provided in the main text or supplementary material should be sent to the corresponding author. The scripts used for unsupervised clustering and gure generation are deposited to https://github. com/ardadurmaz/mds_latent and is publicly available. Cluster assignment tool user guide. available web access allows users to select molecular and cytogenetic features (named genomic pro le) and obtain survival probability and probability of enrichment of such features in each cluster.
Study design and ethics The NordiNet® International Outcomes Study (IOS; NCT00960128) and ANSWER Program (NCT01009905) were observational, multicentre studies monitoring the real-world long-term outcomes of GH treatment (Norditropin®, Novo Nordisk A/S, Denmark) in children and adults. The studies were performed between 2002 and 2016 and involved 676 clinics in 23 countries across Europe, the Middle East and the USA  (18, 19). The patient population reported here comprises the safety analysis set (SAS) including all patients with NS under 18 years of age with at least one Norditropin® prescription recorded. The SAS included both GH-naïve and non-naïve patients. Both studies were conducted with approval from relevant ethics committees, written consent was obtained from all patients after full explanation of the purpose and nature of all procedures used, and pseudonymisation of all data was carried out in accordance with the Declaration of Helsinki, Guideline for Good Pharmacoepidemiology Practices, and regulatory requirements.
Safety assessments Safety events were reported by treating physicians in a case report form and are defined as a collection of serious adverse events (SAEs), serious adverse reactions (SARs; considered related to GH treatment either by the reporting physician or the study sponsor), and non-serious adverse reactions (NSARs; related to GH treatment). Events that were non-serious and not related to GH treatment were not included in this report to maintain compatibility of datasets from NordiNet® IOS and ANSWER Program. CV comorbidities reported at baseline and diagnosed throughout the duration of the studies were also recorded.
Statistical analysis All code for statistical analyses was written using the SAS 9. 4 software. Baseline and safety data were summarised using descriptive statistics. Statistical comparison of baseline characteristics between the SAS and patients who experienced at least one safety event was carried out with Fisher's test for categorical variables, and with t-test or Mann-Whitney U test (when the normality assumption was not met) for continuous variables. A statistically significant difference was defined as p<0. 05.
Baseline characteristics Of the 17,995 and 20,204 paediatric patients registered in NordiNet® IOS and the ANSWER Program, respectively, 156 and 265 had NS. Of these, nine children were excluded owing to missing valid data about GH dose (missing start or stop date of GH treatment or missing dose; n=7) or GH exposure (no treatment periods reported; n=2). Thus, the SAS comprised 154 patients in NordiNet® IOS and 258 in the ANSWER Program (Supplementary Figure  1 ). The combined SAS of 412 children with NS consisted of 70. 9% males and 29. 1% females with a mean (standard deviation; SD) age of 9. 29 (3. 88) years and height SDS of -2. 65 (0. 95) at baseline (Table  1 ). Mean (SD) baseline GH dose was 0. 044 (0. 014) mg/kg/day, and average GH dose during childhood was 0. 047 (0. 014) mg/kg/day.
There were no statistically significant differences in baseline characteristics between patients in the SAS and those experiencing safety events (n=22; Table  1 ), with the exception of baseline height SDS (p=0. 0322). The most common concomitant medications were central nervous system stimulants (12. 4%), antihistamines (6. 6%) and thyroxine replacement (5. 1%) (Supplementary Table  1 ). Anti-hypertensive treatment was reported in four patients (1. 0%) who received diuretics, of which three (0. 7%) were also prescribed angiotensin-converting enzyme inhibitors. Steroid drugs were prescribed to 20 patients (4. 9%).
Funding: This work was supported by  Novo Nordisk Health Care AG. Acknowledgements: Statistical analyses were performed by  Jean-Marc Ferran  ( Qualiance ApS ), funded by  Novo Nordisk Health Care AG. The authors thank  Anna Camilla Birkegård, PhD  ( Novo Nordisk ), for her review and input to the manuscript. Medical writing and editing support was provided by  Sonia Vyskocilova, PhD ,  Richard McDonald  and  Rosalind Perrett  of  Ashfield MedComms , funded by  Novo Nordisk Health Care AG.
Advances in the analysis of amplicon sequence datasets have introduced a methodological shift in how research teams investigate microbial biodiversity, away from sequence identitybased clustering (producing Operational Taxonomic Units, OTUs) to denoising methods (producing amplicon sequence variants, ASVs). While denoising methods have several inherent properties that make them desirable compared to clustering-based methods, questions remain as to the influence that these pipelines have on the ecological patterns being assessed, especially when compared to other methodological choices made when processing data (e. g. rarefaction) and computing diversity indices. We compared the respective influences of two widely used methods, namely DADA2 (a denoising method) vs. Mothur (a clustering method) on 16S rRNA gene amplicon datasets (hypervariable region v4), and compared such effects to the rarefaction of the community table and OTU identity threshold (97% vs.
99%) on the ecological signals detected. We used a dataset comprising freshwater invertebrate (three Unionidae species) gut and environmental (sediment, seston) communities sampled in six rivers in the southeastern USA. We ranked the respective effects of each methodological choice on alpha and beta diversity, and taxonomic composition. The choice of the pipeline significantly influenced alpha and beta diversities and changed the ecological signal detected, especially on presence/absence indices such as the richness index and unweighted Unifrac. Interestingly, the discrepancy between OTU and ASV-based diversity metrics could be attenuated by the use of rarefaction. The identification of major classes and genera also revealed significant discrepancies across pipelines. Compared to the pipeline's effect, OTU threshold and rarefaction had a minimal impact on all measurements.
Clustering based on 97% identity reduces the size of the raw 16S rRNA dataset, and decreasing the computational requirements for analysis  [12]. It also reduces the impact of sequencing errors in downstream diversity estimations, as erroneous sequences are likely to be merged with correct sequences  [10, 13]. Recently, a methodological shift has occurred with the increased use of denoising methods, producing exact sequence variants or amplicon sequence variants (ASVs,  [14] ) instead of producing clusters. Denoising methods generate an error model based on the quality of the sequencing run and use this model to distinguish between the predicted "true" biological variation and that likely generated by sequencing error  [7, 15]. Remaining 'true' sequences varying from as little as one single nucleotide are then defined as separate ASVs, which is intended to allow for a higher accuracy than 97% identity OTUs.
However, given their construction method, ASVs are not equivalent to "100%-OTUs"  [16]. With the increasing popularity of denoising approaches (hereafter, 'ASV-based methods'), studies have compared the results from clustering ('OTU-based') and ASV-based approaches. Based on mock communities, ASV-based approaches had a higher sensitivity in detecting the bacterial strains present, sometimes at the expense of specificity  [17] [18] [19] [20]. However, studies utilizing soil, rhizosphere, and human microbiome datasets found similar overall biological signals  [14, 21, 22]. From these studies, the main weakness of OTU-based approaches appears to be in the accurate detection of alpha diversity, as OTUs often overestimate bacterial richness when compared to ASVs, whereas estimates of beta diversity estimates are typically more congruent between methodologies  [15, 19, 21, 22].
A bacterial community with a few closely related dominant taxa may be more sensitive to the choice of sequence processing method than a community with greater phylogenetic diversity or evenness of abundances. Lastly, while phylogenetic diversity indices (e. g. UniFrac) are typically more efficient at detecting ecological patterns and evolutionary processes than indices based on just operational units (e. g. Bray-Curtis or Jaccard indices, hereafter termed 'taxonomic diversity indices')  [26, 28, 29] , only one of the aforementioned studies compared phylogenetic diversity metrics obtained from ASVs vs. OTUs datasets  [23]. In this study, we compare the influence of two widely used sequence-processing methods, namely the ASV-based DADA2 and the OTU-based MOTHUR pipeline, on the diversity, ecological and compositional patterns of the bacterial communities present in three different microbial assemblages (aquatic sediment, particle-associated freshwater communities 'seston', and the freshwater mussel gut microbiome).
OTU-based pipeline (Mothur) We used Mothur v1. 8. 0  [33]  to assemble, read, and filter the obtained sequences, before clustering them into OTUs. We followed the recommended procedures for Illumina MiSeq sequence data, as it is the methodology most likely to be used by researchers (https://mothur. org/wiki/miseq_sop/, December 2020). Briefly, after merging the forward and reverse reads, we screened the sequences and removed those of unusual length or that contained ambiguous bases. Unique sequences were then aligned to the Silva 16S rRNA gene database v. 138  [34] , and poorly aligned reads were removed. Sequences were classified using the Wang method  [35]  in Mothur through the same database to remove all non-prokaryotic sequences and sequences that were not classified to the Kingdom level.
Chimeras were determined and removed using the chimera. vsearch command with default parameters. OTUs were assembled based on 97%-and 99%-identity, and OTU tables were constructed before retrieving the consensus classification for each OTU. The most abundant sequence within each OTU was selected as the reference sequence for further phylogenetic analyses. A total of 31,453 97%-OTUs and 58,483 99%-OTUs were detected in the unrarefied dataset of 217 samples, based on 3,420-143,827 valid sequence reads per sample. According to the current SOP, no abundancebased filtering of OTUs was applied before performing rarefaction.
ASV-based pipeline (DADA2) We used DADA2 R-package v1. 20  [7]  on R v3. 6. 3  [36]  following the general tutorial available on the Github of the software (https://benjjneb. github. io/dada2/tutorial. html, November 2020). Forward and reverse reads with >2 or >5 estimated errors, respectively, were filtered and were truncated at the 3' end, where read quality dropped below a quality score of 2 (TrunQ = 2). After separate estimations of error rates on forward and reverse reads, ASVs were predicted and merged using a minimal overlap of 12 bases. Chimeras were removed using the consensus method with the removeBimeraDenovo() function. ASVs with <243 or >263 base pairs were removed. ASVs were classified using Wang's classifier  [35]  and the SILVA 16S rRNA gene database v. 138  [34] , using DADA2's default parameters.
There were a total of 21,606 ASVs in the unrarefied dataset of 217 samples, based on 3,164-112,811 valid sequence reads per sample. Despite the same version of the SILVA database utilized for classification of both OTUs and ASVs, minor differences were observed, with Oxyphotobacteria considered as a class within the ASV dataset, and Planctomycetes in the OTU datasets named as Planctomycetacia in the ASV dataset. To simplify comparison across methodologies, we renamed Oxyphotobacteria as Cyanobacteria, and Planctomycetacia as Planctomycetes within the ASV dataset. Relative abundance normalization and taxonomic diversity. ASV and OTU tables were imported into R v3. 6. 3  [36]  using the 'phyloseq' R-package v1. 30  [37]. The minimal number of sequences within samples before rarefaction were 3164, 3420, and 3400, downstream DADA2 (ASVs) and Mothur (97%-and 99%-OTUs), respectively (S2 Table in S2 File).
12% and 4% of samples have less than 4,000 sequences in ASV-based and OTU-based datasets, respectively. Therefore, the three levels of rarefaction chosen were 1000, 2000, and 3000 sequences in order to compare both DADA2 and Mothur methodologies without losing too many samples. Rarefaction was performed by random subsampling sequences using function "rarefy_even_depth" from 'phyloseq' R-package. Subsequent analyses were performed at each rarefaction level as well as on unrarefied 97%-OTUs, 99%-OTUs, and ASVs tables, for a total of 12 community tables (three ASV/OTU approaches x four levels of rarefaction). Sampling coverage was determined for all analysis tables using Chao's coverage index provided in the 'entropart' R-package v1. 6-8  [38]. Indices of taxonomic alpha diversity (observed richness, iChao1 and ACE richness indices correcting for unsampled operational units, and Shannon alpha diversity) were assessed using 'vegan' R-package v2.
5-7 (for observed richness and Shannon)  [39] , 'entropart' (for iChao1) and 'fossil' R-package v0. 4 (for ACE)  [40]. Following recommendations from Jost  [41] , all alpha diversity indices were expressed in an equivalent number of operational units (i. e. , equivalent numbers of species, also known as Hill numbers). The evenness of the abundance of operational units was measured using Bulla's O index, which is less affected by the number of species than other evenness indices  [42, 43]. Taxonomic beta diversity was assessed using complementary indices based on Jaccard and Bray-Curtis dissimilarities, using 'vegan'. While Jaccard is solely based on the presence/absence of operational units, Bray-Curtis gives more weight to the most abundant operational units.
Phylogenetic diversity ASV and OTU reference sequences were incorporated into the GreenGenes 99% phylogenetic tree v13. 8  [44] , using SEPP software  [45]  implemented in QIIME2  [46]  using default parameters. The obtained bacterial phylogenetic tree was then pruned using 'ape' R-Package v5. 5  [47]  to remove all tree leaves absent from our dataset while maintaining the tree's structure. This method allowed for a more accurate tree structure than a de-novo tree reconstruction using short sequences. Phylogenetic beta diversity was then assessed using the weighted (W-) and unweighted (U-) versions of the Unifrac index, using 'GUniFrac' R-package v1. 3  [48].
Statistical analyses Statistical analyses were computed using R, and data visualization was made using 'ggplot2' Rpackage v3. 3. 5  [49]. Differences in alpha and beta diversity values across different methodologies (ASVs vs. 97%-OTUs vs. 99%-OTUs), rarefaction level (no rarefaction vs. rarefaction to 1,000 vs. 2,000 vs. 3,000 sequences), and OTU threshold (99% vs. 97% identity) were assessed using pairwise Wilcoxon signed-rank tests allowing for the identification of a significant difference across methodologies. Spearman's and Pearson's correlations in alpha diversity metrics were computed to measure the correlation strength for these values across methodologies. Similarly, correlations between beta diversity indices were measured using Spearman's and Pearson's correlations in Mantel tests available in 'vegan'.