dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical Analysis: Data pre-processing had been performed by Metabolon, i. e. , the values were normalized in terms of raw area counts. So, for a single-day run, this was equivalent to the raw data. For the statistical analysis from the generated UPLC-MS-MS untargeted data, data normalization was performed using the EZinfo v. 3. 0. 3 (Umetrics, Sweden) tool applying the normalization of Pareto-scaling where the x-variables were centered and scaled to "Pareto variance. "  [51] Data normal distribution from RB powder and children's plasma studies were checked using Metaboanalyst before and after normalization. The principal component analysis (PCA) was performed for the plasma lipidome data, two partial least squares-discriminant analysis (PLS-DA) were conducted for the RB data considering all the data and a subset with the polar lipids and oxylipins, the Variable Importance in Projection (VIP) compounds were selected considering a score >2, and S-plot was performed by Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA).
Acknowledgements The authors thank  Dr Anna McClung  from the  USDA Agricultural Research Service Dale Bumpers National Rice Research Center  for supplying the rice bran used in the human feeding study. Funding for this study was provided  National Institute of Food and Agriculture (NIFA ;  2016-67001-24538 , Ryan), the  Foundation for Research Support of State of Rio de Janeiro (FAPERJ)  ( 26/010. 100988/2018 ; 26/202/709-2018 ;  26/201. 848/2019 ; 26/200. 171/2022 ), National Council for Scientific and Technological Development (CNPq)  ( 427116/2018-0 ;  310343/2019-4 ) and supported by  UNIRIO, Coordination for Improvement of Personnel with Higher Education (CAPES)  (financial code  001 ) and sabbatical scholar stipend was provided by  Montpellier University of Scholarly Excellence (MUSE).
Data Availability Statement Plasma and RB variety metabolomics datasets are available publicly as supplemental files in Li, K. J. , Borresen, E. C. , Jenkins-Puccetti, N. , Luckasen, G. , Ryan, E. P. , Navy bean and rice bran intake alters the plasma metabolome of children at risk for cardiovascular disease. Frontiers in Nutrition 2018, 4 and Zarei, I. , Luna, E. , Leach, J. E. , McClung, A. et al. , Comparative rice bran metabolomics across diverse cultivars and functional rice gene -bran metabolite relationships. Metabolites 2018, 8, 63.
Median and interquartile range with whiskers extending to the minimum and maximum values. D False positive rate and sensitivity of barcode detection. Seven MEF clones with known DRAG barcodes were mixed in different numbers, and the input cell numbers of all MEF clones were compared to experimentally determined numbers upon PCR, sequencing, and analysis. The number in circles corresponds to MEF clone numbers; red and blue circles indicate technical replicates. The gray area indicates lower thresholds for barcode detection as used during data processing. E Bar graphs depicting the number of nucleotides inserted and deleted between the V, D, and J segments. All summary statistics in this graph are derived from an experiment using four adult male DRAG mice. Source data are provided as a Source Data file, while larger datasets and associated source code are available at: https://github.
4D ). To distinguish between these scenarios, we analyzed the number of DRAG barcodes in the blood myeloid cell compartment over time. To this end, we computed the clonal diversity in sequential blood samples using Renyi entropy indexes (Fig. 4E ) and modeled the change in diversity as a function of time using generalized mixed models, accounting for sampling (Supplementary Methods). We found that a generalized linear mixed model with a breakpoint showed the best fit to the experimental data (Supplementary Fig. 6B ). Applying this model to several diversity indices (Fig. 4E , Supplementary Fig. 6C , Supplementary Table  9 ) showed a highly consistent trend over time. Specifically, in the first 7 months following DRAG barcoding, the number of clones contributing to the myeloid compartment decreased over time, consistent with the turnover of shorter-lived cells that were labeled using the ubiquitous CagCre driver (Fig.
5E, F ), with several clusters enriched in either young or aged mice (Fig. 5E, F ). Specifically, these analyses suggest that LT-HSCs are less frequent in aged mice (Fig. 5F ), whereas four clusters were only found in aged mice (Fig. 5E, F ). A common feature of these ageing-associated clusters was the co-expression of the MPP3, MPP4, and MPP5 gene signatures (Fig. 5B, C ) and expression of genes associated with mature myeloid cells (CD74, Ngp, Cll5, Fig. 5D ). Pseudotemporal ordering of the data using a diffusion map approach  42  predicts that HSPCs from agedassociated clusters represent a more differentiated cell state as compared to HSPCs from young mice (Fig. 5G ). This prediction was corroborated by a supervised annotation approach in which we mapped our cell clusters onto an independent reference dataset  43  (44,802 c-kit + and c-kit + sca1 + cells), in which we observed an age-related reduction in the number of cells that mapped to LT-HSCs, while cells from ageassociated clusters increasingly mapped to lineage-restricted progenitors including, GMP, MEP, and CLP (Fig.
The percentage of barcodes in each of the six possible classes is depicted. The barplot represents the median value, while error bars show the standard deviation representing the interquartile range between mice (n = 3). Each point represents a different mouse. D Same as (C) but depicting the total contribution of all barcodes in a given class to the production of either HSPC, MP, or M. The raw data showing the proportions of cells in each cluster are given in Supplementary Data 6. E Number of myeloid cells produced per barcode for n = 4 male DRAG mice (251 barcodes), 15 months post-induction. Colors represent individual mice. The boxplot represents the median and interquartile range. The whiskers extend to 1. 5 times the interquartile range values. Source data are provided as a Source Data file, while larger datasets and associated source code are available at: https://github.
D Top five differentially expressed genes for each cluster. Differential gene expression analysis was performed using a logistic regression test as implemented in the Seurat R package, and Bonferroni correction was applied to account for multiple testing. E Density plot showing the proportional abundance of cells within the UMAP embedding as a function of age. F The proportional abundance of cells among clusters at 6-7 months, 12 months, and 18-19 months old. The raw data showing the proportions of cells in each cluster are given in Supplementary Data 6. G Pseudotime projection of the data with cells organized into clusters as in (C). Pseudotime inference was performed using a diffusion map-based approach as implemented in the R package destiny  42. H Label transfer for supervised annotation of cell state.
scRNAseq atlas of hematopoiesis (left-reference dataset) comprises 44,802 c-Kit+ and c-Kit+ Sca1+ hematopoietic progenitors  43. Cell clustering and supervised assignment of cluster identity on this reference atlas were taken from ref. 67. scRNAseq data from our study (query dataset) was then mapped onto this dataset using Seurat's FindTransferAnchors and TransferData methods. I Barplot showing the relative proportion of cell-state definitions obtained by label transfer mapping. The raw data showing the proportions of cells in each cluster are given in Supplementary Data 6. Source data are provided as a Source Data file, while larger datasets and associated source code are available at: https://github. com/ TeamPerie/UrbanusCosgrove-et-al-DRAG-mouse. sustained by overlapping waves of HSPC activation, with large variations in the cellular outputs of each differentiation-active HSPC clone.
5, 12, and 19 months. Differential expression analysis was performed using a logistic regression test as implemented in the Seurat R package. Bonferroni correction was applied to correct for multiple testing C Pathways enriched in HSPCs at different ages. Pathway analysis was performed using the enrichR R package using a variation of Fisher's exact test (two-sided), which also considers the size of each gene set when assessing the statistical significance of a gene set  68. D Expression of the aged HSPC gene signature across all cell clusters. The gene signature was obtained by differential expression analysis for all HSPCs between young (6. 5 months) and aged (19 months) mice. Genes that are upregulated in aged HSPCs are aggregated into the aged HSPC signature. For each cell, the gene signature score was calculated as the background corrected mean expression across all genes in the signature using the AddModuleScore method in Seurat.
Source data are provided as a Source Data file, while larger datasets and associated source code are available at: https:// github. com/TeamPerie/UrbanusCosgrove-et-al-DRAG-mouse. indirect evidence for such a model. Furthermore, evidence that inflammation pushes LT-HSCs to differentiate  [52] [53] [54]  and that ageing induces proliferative JAK/STAT signaling in HSPCs  55  are in line with this proposed mechanism. Of note, the changes in HSPC composition from scRNAseq were not consistent with changes in population dynamics when defined using surface markers (Fig. 7 ), suggesting that further work is needed to develop a unified definition and nomenclature for HSPCs that is consistent throughout the adult and aged hematopoiesis. As the bone marrow niche has also been shown to change upon ageing  56  , extrinsic factors may also contribute to the increased number of active long-term repopulating clones in aged mice.
5, clone M1/70, eBioscience, 1/100), Ly6C (APC, HK1. 4, Thermofisher, 1/200), Ly6G (BV510, RUO, Biolegend, 1/100), Siglec F (PE CF594, RUO, BD 1/100), F4/80 (alexa 700, clone BM8, Ozyme, 1/100), in addition to a lineage cocktail in PeCy7 (B220 clone RA3-6B2 Biolegend, CD3, clone 17A2 Biolegend, CD11c clone N418, eBioscience, NK1. 1 clone PK136 Biolegend, Ter119 clone TER-119 BD Biosciences) and DAPI (1/1000) as live/dead marker. All cells were analyzed on a Ze5 (Bio-Rad) plate-reader cytometers. Data analysis was performed using FlowJo v10. 2 software (TreeStar) and Prism v9.
Acknowledgements We would like to thank  R. Bin Ali  for blastocyst injections,  Dr. R. Gerstein ,  Dr. K Vanura , and  Dr. S. Gilfillan  for sharing reagents, and  M. Hoekstra  for sharing drawings. We thank past and present members of the  Schumacher lab , in particular  Dr. S. Naik ,  Dr. C. Gerlach , and  Dr. J. Rohr , for valuable discussions. We thank  Dr. K. Duffy ,  Dr. R. de Boer ,  Dr. L. Riboli-Sasco ,  Dr. P. Krimpenfort ,  Dr. J Jonkers , and the  Perié team  for helpful discussions. We thank the  Curie  flow cytometry, next-generation sequencing, and animal facility from both  NKI  and  Institut Curie. The study was supported by an  ATIP-Avenir grant  from  CNRS  and  Bettencourt-Schueller Foundation  (to L. P. ), grants from the  Labex CelTi-sPhyBio  ( ANR-10-LBX-0038 ), and  Idex Paris-Science-Lettres Program  ( ANR-10-IDEX-0001-02 PSL ) (to L.
P. ). As well as funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme ERC StG 758170-Microbar  (to L. P. ) and  ERC AdG Life-His-T  (to T. S. ). A. M. W. and T. M. were supported by  ERC CoG 724208. J. C. was supported by a  Foundation ARC fellowship  and by the  Agence Nationale de Recherche  ( DROPTREP :  ANR-16-CE18-0020-03 ).
Data availability All data and code are available at https://github. com/TeamPerie/ UrbanusCosgrove-et-al-DRAG-mouse. git and https://zenodo. org/ record/7569284#. Y-aCE3bP1D8. Raw sequencing (scRNAseq and lineage tracing) data has been deposited into the Zenodo database in. fastq file format or as. tsv cellranger outputs  [58] [59] [60] [61] [62] [63] [64] [65] [66]. Links to download these data are provided in the source data file. Preprocessed data are available at https://github. com/TeamPerie/UrbanusCosgrove-et-al-DRAGmouse. git along with source code to generate figures from these data. Source data for all other data types (e. g. , flow cytometry) are provided in the supplementary information and the source data file. Sequencing data associated with this paper have been deposited at NCBI GEO using accession code GSE226131.
Then, samples were stained with the desired antibodies as previously described  (73, 74). Data were analyzed with FlowJo software (version 10. 8. 1, BD) and the gating strategy provided in Supplemental Figure  7. Total immune cells were defined as cells with the surface marker CD45, excluding debris and gating on singlets to identify live cells. From CD45 + cells, the cells within the CD45 + B220 -CD4 - CD8 -Ly-6C -Ly-6G -CD11b + F4/80 + gate represented macrophages. B cells were defined as cells with the B220 surface marker, and T cells were defined as cells with the surface markers CD4 and CD8. The cells within the CD45 + B220 -CD4 -CD8 -Ly-6C + Ly-6G + gate represented neutrophils. Plasmids and molecular biology. Full-length and truncated human NLRP3 (94-979; 1-220; 1-389; 1-574; 575-979) were subcloned into pCMV-3Tag-1 as described (ref.
During nigericin treatment, cells were incubated with 50 μM biotin. After that, both floating and adherent cells were collected in PBS with 1 mM EDTA. The resulting cell pellets were lysed in the lysis buffer (50 mM Tris, pH 7. 4, 500 mM NaCl, 0. 4% SDS, 5 mM EDTA, 1% Triton X-100) freshly supplemented with 1 mM DTT and 1 tablet of cOmplete Mini EDTA-free Protease Inhibitor Cocktail (Roche, 11836170001) per 10 the pLenti-BASU-Plk1-Myc-DDK-P2A-Puro plasmid used for lentiviral transduction. The BASU-Plk1 sequence was further cloned into a pCMV5-HA vector to create pCMV5-BASU-Plk1. Antibodies. Antibodies against NLRP3 (AdipoGen Life Sciences, AG-20B-0014-C100; MilliporeSigma, HPA012878), IL-1β (R&D Systems, AF-401-NA), caspase 1 (Santa Cruz Biotechnology, SC-154), HA tag (Santa Cruz Biotechnology, y-11), Flag tag (MilliporeSigma, F1804), PLK1 (Abcam, ab17056), β-actin (Cell Signaling Technology, 3700S), and ASC (Enzo Life Sciences, AD1-905-173-100) were used for Western blotting.
Incubation of tRPE PLK1 AS cells overnight with 20 μM of the purine analog 3MP-PP1 (Cayman Chemicals, 17860) was done to induce cell-cycle arrest, and cells in cell-cycle arrest were counted over 3 fields of view. Cells were cultured at 37°C in 5% CO 2 in a humidified incubator. in the linear ion trap. Synchronous precursor selection was used to select the top 10 most abundant fragment ions from each MS/MS for further fragmentation in the high energy collision cell using high-energy collisional dissociation (HCD) (NCE: 65%). The Orbitrap analyzer measured all m/z values, and the relative abundance of each reporter ion and all fragments in each MS step with a resolution at 60,000. Raw data processing was performed using Proteome Discoverer, version 2. 4 (Thermo Fisher Scientific). The data were searched against the Uniprot Mouse database and a database of common contaminants by the Mascot search algorithm (Matrix Science).
1. 2) qPLEXanalyzer (version 1. 12. 0) package's "normalizeScaling" function. The central median normalization was used for log-scaled intensities. The "ComBat" function (sva package, version 3. 42. 0) was used for batch correction. Paired DE analysis identified 247 upregulated proteins and 172 downregulated proteins with a cutoff absolute log 2 fold change of greater than 0. 4 (equal to an absolute 1. 3-fold change) and a Benjamini-Hochberg-corrected adjusted P value (adj. P. Val) of less than 0. 05. The total differentially interacting proteins with statistical significance (419) were used for GO unbiased analysis with clusterProfiler (version 4. 2. 2, R package). Subcellular localization analysis used the data from The Human Protein Atlas (https://www. proteinatlas. org/humanproteome/subcellular), and mouse proteins were converted to human proteins and then matched to the main location.
ELISAs were performed according to the manufacturer's instructions. Meso Scale Discovery (MSD) assays were performed by the core biochemical assay laboratory of Cambridge University Hospitals. The detection limit of mouse IL-1β by MSD was 1. 2 pg/mL. Statistics. Statistical analysis was performed with GraphPad Prism. Data are expressed as the mean ± SEM in all plots. Comparisons of the 2 different groups were analyzed by 2-tailed, unpaired t test or by paired t test as indicated. For more than 2 groups, 1-way ANOVA followed by Tukey's test (for 1 variable) and 2-way ANOVA with Šidák's multiple-comparison test (for 2 independent variables and multiple groups) were used. When data did not follow a normal distribution, nonparametric tests were used to analyze the data. A P value of less than 0.
05 was considered statistically significant. Study approval. The animal studies were performed following UK Home Office regulations, under approved Home Office project licenses PPL PA4BDF775 and PP9485757 (United Kingdom). turer's instructions (Duolink). Live cells were fixed with 4% PFA and then subjected to the PLA assay. Images were acquired with a Carl Zeiss LSM 700 microscope. PLA signals per cytoplasm of a cell were acquired using Duolink analysis software. Co-IP assay. Cell lysates from HEK293T cells were collected and lysed in NP40 lysis buffer (500 mM Tris HCl, pH 8, 500 mM MgCl 2 , 2. 5 M NaCl, 0. 25 M EGTA, pH 8, 5% NP40) supplemented with 1 mM Na 3 VO 4 and 50 mM NaF and 1 tablet of cOmplete, Mini, EDTA-free Protease Inhibitor Cocktail per 10 mL buffer (Roche). For co-IP using the HA-tag, proteins were incubated with rabbit anti-HA antibody (Santa Cruz Biotechnology, sc-805) for 2 hours, rotating at 4°C followed by overnight incubation with M-280 sheep anti-rabbit IgG Dynabeads (Thermo Fisher Scientific, 11203D).
125 nM for 180 seconds to measure the association phase and then transferred to buffer for 180 seconds to measure the dissociation phase. Biosensors were regenerated between each reading using 10 mM glycine (pH 2. 0) for 5 seconds and neutralized in assay buffer for 5 seconds; this was repeated 3 times for each cycle. Subtraction of reference sensor data, and Savitsky-Golay filtering and kinetics parameter derivation were conducted in FortéBio Data Analysis software (version 8. 0). Data were graphed using GraphPad Prism (GraphPad Software). Nonspecific binding of PLK1 to empty anti-GST biosensors and recombinant GST alone was negligible (data not shown). Western blotting. Supernatants of cells treated with inflammasome-activating stimuli were collected in serum-free media. TCA protein precipitation was performed on supernatants to detect cleaved IL-1β and caspase 1.
Acknowledgments This work was supported by  British Heart Foundation (BHF)  fellowship grants ( FS/14 /28/30713  and  FS/SBSRF/22/31036 , to XL); a  BHF  project grant ( PG/17/69/33229 , to XL); a  BHF  PhD stu-J Clin Invest. 2023; 133(21 ): e162129  https: //doi. org /10. 1172/JCI162129  Medicine,  University of Cambridge, Cambridge, UK)  for her assistance with virus transduction. We thank the biophysics facility and the facility manager,  Katherine Stott  ( Department of Biochemistry, University of Cambridge ), for assistance in performing Biolayer interferometry experiments. We thank the people who provided us with the research materials listed in Methods. Address correspondence to:  Xuan Li , The  Victor Phillip Dahdaleh  Heart and  Lung Research Institute, University of Cambridge, Papworth Road, Cambridge, United Kingdom , CB2 0BB, UK.
and a premature stop codon at position 375 and leading to a truncated protein of only 124 amino acids (Figure  1 ). The nucleotide mutation was also confirmed by Sanger sequencing for exon 2 and 4 using a commercial polymerase chain reaction sequence-based typing kit (Life Technologies, Shanghai, China). The nucleotide sequence of the HLA-DPB1*1229:01N allele has been submitted to the GenBank nucleotide sequence database and the accession number OK501158 was assigned. The complete HLA genotype of the sample was HLA-A*24:02:01,.
The novel allele HLA-DPB1*1455:01N carries one deletion of a C nucleotide at position 8993 in exon 3 when compared with the DPB1*05:01:01:01 sequence (Figure  1 ). This deletion makes a synonymous change at residue 140 as GTC (Valine) become GTG (Valine) and introduces a premature stop codon occurring at residue 145. This frameshift impacts the expression of the protein on cell surface, thus it is a new null allele. The nucleotide sequences of the exons 2, 3, and 4 of this new allele were submitted to the GenBank database (Accession No. OP297269) and to the IPD-IMGT/HLA Database 2 (Submission No. HWS10062736). The name HLA-DPB1*1455:01N was officially assigned by the WHO Nomenclature Committee for Factors of the HLA System 3 in January 2023. This follows the agreed policy that, subject to the conditions stated in the most recent Nomenclature Report, names will be assigned to new sequences as they are identified.
Acknowledgments The work performed at LPC2E is supported by  CNES APR. We benefited from the computing resources provided by  CaSciModOT. This work was granted access to the  HPC/AI resources of TGCC  under the allocation 2021-A0100412428 made by  GENCI. L. B. was supported by funds from  Région Centre Val de Loire (France). We thank  E. Odelstad  for the support he provided for the identification of the plasma drift velocity in the chamber.
Data Availability Statement Datasets for this research are available at  Bucciantini (2022) , together with a detailed explanation on how to use them. The model used for the production of such dataset is described in Section 3. 1. It is based on the model implemented by  Mangeney et al. (2002). The 1D-1V Vlasov-Poisson version of the model, which corresponds to the one we use in our investigation, is described in  Henri et al. (2010).
Field sampling Samples were collected at 18 roosts (coded with a 3-letter code) across Reunion Island (Fig. 1 ) and during different seasons. Among these roosts, six (AOM, CIT, PBV, RAC, STJ, and TM5) were only sampled once throughout the study, because of opportunistic sampling (Table  S1 ). The remaining roosts were sampled multiple times from October 2018 to March 2020. Specifically, we collected samples from eight roosts (ESA, MON, PSR, RBL, RPQ, STM, TGI, and VSP) during three biological seasons: (i) the pregnancy period (austral summer) from late October to early December 2018), (ii) the non-reproductive period (austral winter) in June and July 2019, and (iii) the putative mating period in March 2019 and 2020  (Aguillon et al. , 2023, Table   S1 ). Three roosts (EGI, RES, and TBA) were quasi-empty during the nonreproductive/winter period, explaining the lack of data during this season.
3'). PCR program included an initial denaturation step (95°C for 2 min), followed by 35 cycles of denaturation (95°C for 30 sec), annealing (50°C for 30 sec), elongation (72°C for 1 min 30 sec), and a final elongation step (72°C for 7 min). PCR products were Sanger-sequenced by the GENOSCREEN platform (Lille, France). The D-loop chromatograms were visually checked using Geneious 9. 1. 8 (Biomatters Ltd, Auckland, New Zealand) and sequences were aligned using CLC Sequence Viewer 7. 6. 1 (Qiagen Aarhus A/S, Aarhus, Denmark). DNA extracted were genotyped by GENOSCREEN, using a panel of 12 previously described microsatellite markers, according to the protocol and primers of  Dietrich et al. (2019).