dataesr/scientific-paragraphs-categorization huggingface dataset:
Next, the means of each value were calculated and value ranges for the expression of each marker were defined as 0-0. 1, 0. 1-0. 3, 0. 3-0. 7, 0. 7-0. 9 and 0. 9-1. 0 corresponding to 'negative' , 'low' , 'intermediate' , 'positive' and 'high' expression levels, respectively (Extended Data Fig. 3d ). In the last step, each metacluster was assigned to a unique B cell subset based on the expression pattern of the six markers and the current advances of human B cell immunophenotyping  28. Relative frequencies of each subset by donors were also obtained from the FlowSOM analysis (Extended Data Fig. 3e ). t-distributed stochastic neighbor embedding. A t-SNE algorithm was used for visualizing the multidimensional flow dataset in a dimension-reduced data space to compare B cell subset distribution in pRD compared to HCs.
t-SNE analysis was performed on the concatenated dataset defined in the FlowSOM analysis with the same parameters (CD21, CD24, CD27, CD38, IgD and IgM) using default settings. Establishing two-dimensional sequential gating strategy for B cell subset identification using HyperFinder. To extend immunophenotyping to the entire patient cohort a two-dimensional sequential gating was constructed using HyperFinder FlowJo plugin. Briefly, HyperFinder was applied on the concatenated dataset defined in the FlowSOM analysis with the same parameters (CD21, CD24, CD27, CD38, IgD and IgM) for searching all combinations of available parameters to find a two-dimensional convex polygon gating strategy that most accurately distinguishes the target B cell subset from the background B cell populations. Optimal sequential gatings for each of the 16 target B cell subsets were automatically calculated by maximizing Fm-score.
This approach accurately recapitulated FlowSOM analysis on the test dataset (P9, P12, P14, P16, C1, C3, C5 and C7) as demonstrated with high F-measures for each B cell subset (0. 84 ± 0. 06 mean ± s. d. , range 0. 68-0. 96). Therefore, the constructed sequential gatings were utilized on the patients with pRD (14 pRD-Ag and one pRD-N) along with 20 HCs (16 HC-Ag and 4 HC-N) to evaluate their immunophenotype (Extended Data Fig. 3f ). Immunohistochemistry. Immunohistochemistry was performed using anti-CD20 (L26 mouse monoclonal primary antibody, Roche Diagnostics, 0. 3 μg ml -1 ), anti-CD21 (2G9 mouse monoclonal primary antibody, Cell Marque Tissue Diagnostics, 0. 5 μg ml -1 ), anti-CD4 (SP35 rabbit monoclonal primary antibody, Roche Diagnostics, 2. 5 μg ml -1 ) and anti-BCL2 (I24 mouse monoclonal primary antibody, Roche Diagnostics, 2.
The samples were then mounted with Fluoromount-G (0100-01 Southern Biotech) and imaged under a confocal microscope with a resonant scanning disk (Nikon A1R, Nikon Instruments) with Z-sectioning (0. 5 μm), using Nikon NIS-Elements AR Analysis 4. 40 software. Indirect fluorescent antibody assay. For the detection of autoantibodies against cellular antigens, we performed IFA on HEp-2 slides (human epithelial cells, Bio-Rad) following the manufacturer's instructions. Briefly, plasma samples (25 μl) were added to the substrate for 20 min. Then, substrates were rinsed with PBS for 10 min and incubated with 25 μl fluorescein-conjugated (FITC) antiserum for 20 min at room temperature. Slides were then washed with PBS and counterstained with Evans blue dye in PBS for 10 min first and with DAPI for additional 10 min.
To be consistent throughout our studies, we extracted all raw nucleotide sequences from these datasets and reannotated using IMGT/ HighV-QUEST (program v. 3. 4. 17, reference directory release, 201915-3)  61. Unless specified otherwise, all further data processing steps were performed using algorithms of the Immcantation framework 62 , Tidyverse system (H. Wickham and G. Grolemund, R for Data Science) and the R language (v. 3. 6. 3) (Extended Data Fig. 6a ). IMGT annotated data were first converted to a standard adaptive immune receptor repertoire (AIRR) rearrangement format with 'MakeDb. py' script of Change-O package (v. 1. 0. 0)  63. Copy numbers and isotypes were brought back to these repertoire datasets from the original iRepertoire annotations. For subsequent steps, only the CDR1-FWR4 region was kept everywhere.
Single-clone heavy-chain sequences of previously in vitro characterized, functional monoclonal antibodies of the individuals were annotated with IMGT in the same way as iRepertoire data. For the treemap, to visualize diversity, we plotted the relative frequencies of the most abundant 2,500 clones on treemaps categorized by 58 V H gene families. Divergent color hues were assigned to different V H genes unified across all repertoires. Distinct clones sharing the same V H gene were displayed using altered brightness of the same hue. Treemaps were plotted with the 'treemap' function from treemap R package  64. For gene usage, V, D and J gene usages were quantified by calling the 'countGenes' R function (Alakazam v. 1. 0. 2 package) on repertoires. For the SHM profile, SHM levels were measured with the 'observedMutations' function (SHazaM v.
1. 0. 2) for each sequence and the mean of these values was used. The 5-mer nucleotide motif mutability probability values or SHM-targeting model in each compartment was calculated by 'createTargetingModel' function and visualized as linear or circular ('hedgehog') bar graphs using the 'plotMutability' function (SHazaM v. 1. 1. 0). Descriptive indices (D50, Shannon-Wiener and Gini-Simpson) were calculated implementing standard statistical formulas in R. For Bayesian estimation of antigen-driven selection (BASELINe), an established method was followed to quantify antigen selection  [65] [66] [67]. In each sequence dataset (repertoires, random downsampled subsets or clonotype lineages), first the posterior probability density function for every single sequence was calculated by using 'calcBaseline'.
1. 0. 2 package) using sequence length normalization (mode, 'ham'; normalize, 'len'; and first, 'FALSE'). The histogram representing these distances in the repertoire was smoothed and after the first peak, the position of the first non-declining value instead of the previously used local minimum was determined. Subsequently, this position as the distance threshold was used to generate clonotype clusters utilizing the 'DefineClones. py' script (-mode gene-act set-norm len-model ham). Putative closest germline sequences were added to the prepared and clonally grouped data by 'CreateGermlines. py' algorithm (-g dmask-cloned). For identification of the relatives of cloned heavy-chain sequences (ImmChainTracer pipeline), we assembled a pipeline (ImmChainTracer) that collects close relatives of in vitro characterized and IMGT annotated, single-clone, naive B cell-derived heavy-chain sequences in specified repertoires.
Acknowledgements We thank  L. Fesus  and  R. Kiraly  ( University of Debrecen ) for providing recombinant human tissue transglutaminase. We thank  G. Mueller  for her contribution to the experiments ( Johns Hopkins All Children's Hospital/Children's Research Institute ). We thank  B. Schachter  ( Beth Schachter Consulting ) for careful review and editing of the manuscript. We thank  A. Leiss-Piller  ( Immunology Outpatient Clinic ) and  I. Kingyue Chinn  ( Center for Human Immunobiology, Texas Children's Hospital ) for assisting in variant resolution. We thank  H. L. Monforte  ( Johns Hopkins All Childrens Hospital ) for providing a paraffin-embedded spleen tissue block from a healthy individual. This work was supported by the  National Institutes of Health (NIH National Institute of Allergy and Infectious Diseases (NIAID)   5K08AI103035 , sub- R01AI100887-05  and  R01AI153830-05  to J.
Data analysis The analysis of flow cytometry data was performed using FlowJo software (version 10. 7. 1) and FlowJo plugins including FlowSOM (v2. 5), tSNE (v2. 0), Hyperfinder (v0. 6. 2) and DownSample (v3. 0). Data representation and statistical analysis was performed using Graphpad Prism (version 9. 1. 0). BCR repertoire analysis was performed using IMGT/HighV-QUEST (version: 3. 4. 17, reference directory release: 201915-3) and R (version 4. 0. 4, 2021-02-15) on a x86_64-pc-linux-gnu (64-bit) platform. T cell repertoire data was analyzed with Adaptive Immunoseq Analyzer 3. 0 software. For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors and reviewers. We strongly encourage code deposition in a community repository (e.
Data Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A description of any restrictions on data availability -For clinical datasets or third party data, please ensure that the statement adheres to our policy BCR and TCR repertoire sequencing data that support the findings of this study have been deposited in the NCBI submission portal under accession code PRJNA746291. iRepertoire pre-processed data files (iRepertoire_preprocessed. zip), data files further processed by our analysis pipeline (prepared_AIRR_HC. zip and prepared_AIRR_pRD. zip) and the individual IgH variable sequences cloned from single B cells (prepared_AIRR_single_clone_IgH_sequences.
Data availability BCR repertoire sequencing data that support the findings of this study have been deposited in the NCBI submission portal under accession code (PRJNA746291). iRepertoire pre-processed data files (iRepertoire_preprocessed. zip), data files further processed by our analysis pipeline (prepared_AIRR_HC. zip and prepared_AIRR_pRD. zip) and the individual heavy-chain variable sequences cloned from single B cells (prepared_AIRR_single_clone_IgH_sequences. zip) are available on GitHub (https://github. com/blazsop/pRD-data). All additional data needed to evaluate the conclusions in this study are present in the main text or Supplementary Information. TCR repertoire sequencing data are available under accession code PRJNA746291. Source data are provided with this paper.
sRNA accumulation against CaMV, tasiRNA suppression, and TRV-induced gene silencing remain intact in dcp5 and lsm1 RNA silencing is frequently activated in RQC mutants and involves the biogenesis of small RNAs (sRNAs) against endogenous targets (Martinez de  Alba et al. , 2015). To determine whether viral sRNAs profiles and amounts were altered in lsm1 and dcp5 in an RDR6-dependent manner, we analyzed sRNAs in infected Col-0, rdr6, lsm1, lsm1 rdr6, dcp5, and dcp5 rdr6 using sRNA-sequencing. We produced libraries from rosette samples at 21 dpi in duplicates and mapped 18-26 nucleotide (nt) reads to the TAIR10 Arabidopsis reference genome and against the CaMV genome (GenBank V00140. 1). In agreement with previous observations, most viral sRNAs mapped against the highly abundant noncoding 8s RNA (Figure  6, A  and C ).
Plant material and growth conditions All mutants used in this study were in the Arabidopsis thaliana accession Columbia-0 (Col-0) background, which was used as a control for all experiments (Supplemental Data Set 2). Mutants were checked for homozygosity using the primers described in Supplemental Data Set 3. Arabidopsis and N. benthamiana plants were grown in walk-in chambers under standard long-day conditions (120 mE,16-h light/8-h dark cycle) at 22 C day temperature (20 C night temperature) and 65% relative humidity for crossing, propagation, and transient expression assays. For infection experiments, plants were grown under short-day conditions (120 mE, 10-h light/14-h dark cycle) at 22 C day temperature (19 C night temperature) and 65% relative humidity. Light spectra in both conditions ranged from 400 to 720 nm.
5), supplemented with 2% sodium dodecyl sulfate (SDS) and treated with 0. 2-mg/mL Proteinase K. Total DNA was precipitated with isopropanol 1:1 (v:v), and viral DNA levels were determined by quantitative Real-Time polymerase chain reaction (qRT-PCR) and normalized to 18S ribosomal DNA  (Hafren et al. , 2017). RNA extraction from rosette tissue was performed with a Qiagen RNeasy kit and oncolumn DNase I digestion according to the manufacturer's protocol. About 500 ng of total RNA was used for firststrand cDNA synthesis with a Maxima First Strand cDNA Synthesis Kit (Thermo Fisher Scientific Waltham, MA, USA]). qRT-PCR analysis was performed with Maxima SYBR Green/ Fluorescein qRT-PCR Master Mix (Thermo Fisher Scientific) using the CFX Connect Real-Time PCR detection system (Bio-Rad, Hercules, CA, USA) with gene-specific primers (Supplemental Data Set 5).
Afterward, corresponding forward and reverse reads were combined using fastq-join (version 1. 3. 1; https://github. com/ExpressionAnalysis/ea-utils/blob/ wiki/FastqJoin. md) and the option-v for illumina reads. Joint fastq files were size trimmed to obtain only sizes of 18-26 nt by utilizing cutadapt (version 1. 9. 1)  (Martin, 2011)  with the parameters -m 18 -M 26. Read size and quality were checked using FastQC in default mode (version 0. 11. 9; http://www. bioinformatics. babraham. ac. uk/projects/fastqc/). Size trimmed fastq files were further processed as described in  Bente et al. (2021)  using publicly available scripts (https:// github. com/AlexSaraz1/paramut_bot). To create sRNA profiles along the CaMV sequence, the hygromycin phosphotransferase transgenic sequence from  Bente et al.
2021  was replaced by the genomic CaMV sequence (GenBank V00140. 1) by setting the start position to the beginning of the 35s promoter. Reads were aligned to the TAIR10 genome including an extra contig containing the abovementioned CaMV sequence. Alignment was done using Bowtie2 (version 2. 3. 5. 1; Langmead and Salzberg, 2012)  with the options -k 500 -no-unal. For read counts in the TAS genes and its targets, featureCounts  (version v1. 6. 3; Liao et al. , 2014)  from the Subread package (http://subread. sour ceforge. net/) was used with the options -t gene -s 1 -M on the aligned bam files  (Liao et al. , 2014). Graphical representation was achieved using R. For profile comparisons along the CaMV sequence, the axes were adjusted to 8,000 and 6,000 RPM on the positive and negative strands, respectively.
Acknowledgments We thank  Aayushi Shukla  for her valuable input on this manuscript. We thank  Joanna Kufel  for sharing the lsm1a/b double mutant,  Cecile Bousquet-Antonelli  for the xrn4 mutant,  James Carrington  for the rdr and dcl mutants, and  Kazuki Motomura  and  Yuichiro Watanabe  for the DCP1-GFP line.
Funding This study was supported by the  Swedish Research Council VR  (grant number  2017-05036 ) and  Knut and Alice Wallenberg Foundation  (grant number  2019-0062 ) for A. H. J. H. and A. B. were supported by grants from the  Knut and Alice Wallenberg Foundation , the  Swedish Governmental Agency for Innovation Systems and Bio4Energy , a  Strategic Research Environment  appointed by the  Swedish government. D. G. was funded by an attractivity grant from the  NetRNA LabEx ,  ANR-10-LABX-0036_NETRNA. The authors acknowledge support from the  National Genomics Infrastructure in Stockholm  funded by  Science for Life.
However, the data on causal dose-effect relationships remain limited and findings generated from field studies are scarce. In this context, Adverse Outcome Pathways (AOPs), and especially AOPs focused on reproductive endpoints have the potential to: (i) deliver a mechanistic understanding to enable establishment of causal links (not simply associations) between radiation dose (or dose rate) and effect on specific reproductive endpoints; and (ii) organize and evaluate research findings that demonstrate lines of evidence underpinning regulatory benchmarks.
Objectives The aim of the present work was to review the available data for radiation effects in a range of organisms to assess the potential for developing AOPs for radiation-mediated effects on reproduction and subsequent population changes. The work focused on a few model organisms that represent different reproductive strategies and sensitivities to radiation, and for which data from laboratory studies on radiationmediated effects were well developed. The work (Figure  2 ) consisted of: (1) developing a set of taxon-specific AOPs; (2) identifying common AOP components (events) to develop consensus AOPs (cAOPs) with a broad taxonomic applicability; and (3) discussing how reproductive-relevant AOPs could be applied in radiation (radioecological) research, environmental health assessment and radiological protection.
Case Studies A set of case studies (Figure  2 ) were developed to illustrate the diversity and commonalities in AOP components across laboratory model species representing different taxa (primary producers, nematodes, earthworms, crustaceans and fish). Each case study (see Supplementary Table  S1  for details) represented a different taxonomic group and was developed using available data collected through narrative reviews. The species groups covered were chosen on basis of available knowledge from literature and data generated by the WG participants, rather than defining a broad as possible taxonomic applicability domain a priori. Available data were organized within an AOP framework and assessed for conformity to the B-H criteria at the taxon-specific level using the Guidance Document for developing and evaluating AOPs (OECD 2018) and the resultant AOPs were submitted to the AOP-wiki, when considered sufficiently mature (see Supplementary Table  S1 ).
Conceptual AOPs An AOP network consisting of four interconnected AOPs has been proposed and submitted to the AOPwiki (AOP #386, #387, #388 and #435) for radiation-induced reproductive effects in primary producers. This AOP network (see Supplementary Table  S1  for details) was developed using data from studies of a few model species, and can be potentially applied to other types of ionizing radiation (e. g. X-rays) or non-ionizing radiation (e. g. UV radiation), that display similar toxicity mechanisms involving oxidative stress and DNA damage. As the KEs were highly recapitulative and relevant to common biological processes, the taxonomic applicability domain can potentially cover most terrestrial and aquatic primary producers that contains chloroplasts and mitochondria at any life stage.
Brief evaluation of data availability/gaps Supporting evidence for this AOP network were captured from various studies with external beta (0. 08-97 mGy h À1 ) and gamma ( 137 Cs and 60 Co, 0. 08-1500 mGy h À1 ) radiation on the microalgae Chlamydomonas reinhardtii, the macrophyte Lemna minor, and the higher plants Pinus sylvestris, Arabidopsis thaliana and Oryza sativa. However, different primary producers may display species/taxon-specific reproduction strategies that give rise to larger diversity in AOP events than that proposed herein. The current AOPs were predominantly concerned with energy supply and genotoxicity induced by radiation. Other sexual reproduction strategies of primary producers to radiation, including oogenesis, flowering, and seed germination, were not included in the proposed AOPs and require additional investigation.
Conceptual AOPs Based on data obtained from laboratory studies, an AOP network consisting of four conceptual AOPs has been proposed and submitted to the AOPWiki (AOP #216, #238, #299 and #311) for radiation-induced reproductive effects in crustaceans. The AOP network (see Supplementary Table  S1  for details) was in general female specific because supporting evidence covers radiation effects on oocytes, ovaries and egg production only. The majority of studies were conducted on clonal females which reproduce via asexual parthenogenesis (without meiosis, without fertilization), including D. magna and other species of the daphnid family  (Gilbin et al. 2008; Marshall 1962 Marshall , 1966;; Parisot et al. 2015; Trijau et al. 2018; Song, Xie, Lee, Brede et al. 2020). The proposed AOPs were considered applicable to female crustaceans that reproduce sexually, as similar decline in egg production was reported in the Kuruma prawn Marsupenaeus japonicus, the giant freshwater prawn Macrobrachium rosenbergii and the marine copepod Paracyclopina nana  (Sellars et al.
2022). Over the last two decades, there have been significant advances in the development of environmental radiation protection. Through projects such as the EC ERICA project  (Larsson 2008; Brown et al. 2016) , UN IAEA programmes  (IAEA 2014)  and the work of the ICRP  (ICRP 2008 (ICRP , 2017)) , environmental radiation protection has already advanced to a stage where decision makers have software and tools, underpinned by robust datasets, to inform proportionate regulation. However, the data on dose-effect relationships remain limited for many organism groups and findings generated from field studies of radiation effects in the environment often deliver observations of associations with radiation exposure rather than demonstrating causality. Also, the focus of environmental protection is generally the protection of populations so evidence on the effects of radiation on reproductive endpoints is crucial.
For the first time, consensus AOPs for radiation effects on reproduction was built through gathering knowledge from several taxon-specific AOPs. This effort could serve as a basis for evaluation of interspecies differences in radiosensitivity through development of quantitative AOPs  (Perkins et al. 2019; Song, Xie, Lee, Tollefsen et al. 2020) , and for identification of species-specific pathways that could explain differences in populations and individual radiosensitivity. Such development would be instrumental for thorough assessment of existing WOE considerations, consolidating the knowledge domain and fill data gaps in a transparent and reproducible way. The information assembled into the AOP networks not only lends itself useful for enhancing the mechanistic understanding of effects underpinning adversity, but can be useful for identifying exposure or effect biomarkers that can specifically and quantitatively characterize stressor-response and response-response relationships for radiation-mediated effects on reproduction  (Chauhan, Stricklin et al.
2020). A key step in such development would be developing qAOPs by quantitating the stressor-response and response-response relationships for individual events identified in the present study. The AOPs and AOP networks discussed here would thus potentially be a source of information to enhance our understanding of interspecies differences in radiosensitivity and how individual AOPs and AOP networks are triggered in a dose rate-and dose-specific manner. Such efforts would ultimately facilitate the transition toward a more mechanistically informed hazard and risk assessment of reproductive impairment, including determination of Relative Biological Effectiveness (RBE) of the different radiation types (alpha, beta, gamma). However, it remains to be determined if our current mechanistic understanding, availability of data and linearization of complex biological mechanisms from controlled laboratory studies into AOPs provides a sufficiently robust model for predicting long-term population effects under dose rates and doses being ecologically relevant and conditions that reflect natural complexity.
Conclusions and future prospects The current work has presented five individual reproductive-and taxon-focused cases studies of ecological relevance, and defined a common set of events into a set of cAOPs. The cAOPs represent the initial effort to capture and harmonize effect information spanning different levels of biological organization and a high number of species into a common knowledge framework that can be used to develop linearized AOPs with a broad taxonomic applicability. This effort, when undertaken, will take advantage of a well-developed scientific consortium, with experts representing diverse areas of expertise, to populate the individual AOPs with a more universal set of data to reduce the apparent bias toward a few well studied environmental model species. Future initiatives are expected to entail structured WOE assessments based on automated literature and data mining  (Song, Xie, Lee, Tollefsen et al.