dataesr/scientific-paragraphs-categorization huggingface dataset:
Children were included in one of the studies. Risks of recurrence or relapse was assessed in just one study and was reported to be 1 of 24 (4%) in patients switched to early oral antimicrobials (i. e. 6 weeks of treatment) and 4 of 77 (5%) in those treated with IV throughout (p 0. 84) (Table  S15 )  [187]. Both studies provided data on fatal outcome and this was observed in 0 of 21 (0%) and 1 of 24 (4%) of patients switched to early oral antimicrobials compared with 5 of 28 (18%, p 0. 06) and 12 of 77 (16%, p 0. 18) of patients treated with prolonged IV treatment  [186, 187]. The corresponding OR was 5. 7 (95% CI 1. 0e31. 3) in favour of early transition to oral antimicrobials (Figs. 15, S19,  and S20 ). However, selection of patients with mild and uncomplicated disease to early transition to oral antimicrobials was not sufficiently accounted for and likely explains the observed survival advantage.
METHODS Human CD34 + HSPCs. PB-CD34 + cells from a RAG2-SCID subject were collected following informed consent (protocol NCT00001405, approved by the NIH IRB as NIH protocol 94-I-0073). Additional information is detailed in supplemental methods. Off-target activity assay. The top forty-eight putative off-target sites for RAG2 were identified using COSMID  32  and tested using rhAmpSeq  33, 34  (Integrated DNA Technologies). Additional information is described in supplemental methods. Colony-forming units (CFU) assay. Single live HSPCs were sorted and analyzed, as previously described  27. Colony genotyping is detailed in supplemental methods.
While lentiviral (LV) gene therapy is being developed for RAG1-SCID  44  (a closely but genetically distinct from RAG2-SCID) and the safety and efficacy of delivering the RAG1 nuclease in HSPCs are being tested (NCT04797260), no alternative therapies, other than allo-HSCT are currently available for patients with RAG2 deficiencies. We report the first feasibility study of gene-correction at the RAG2 locus in human HSCs derived from healthy and RAG2 null patients to assess the safety and efficacy of this strategy. Allo-HCT for RAG1/2-deficient patients shows clinical improvement if a minimum of 20% donor chimerism is achieved  11  , an allele correction threshold that we have achieved and surpassed. We demonstrate that coRAG2-HSPCs can support durable hematopoiesis (18-20 weeks in immunodeficient mice), with only a 2-fold decrease in engraftment potential of corrected RAG2 null patient' cells.
ACKNOWLEDGMENTS We thank  Stanford 's  Binns Program for Cord Blood Research and patients  for providing an invaluable source of  CD34 + HSPCs  for research. We thank the  Stanford FACS Core facility  for providing technical support. We acknowledge BioRender graphic software for helping with highquality schematics. M. P-D gratefully acknowledges the funding support from the  Primary Immune Deficiency Treatment Consortium  ( U54 AI 082973 ), part of the  NCATS Rare Diseases.
DNA was stained using Hoechst (Invitrogen, #1681305) and actin was stained by rhodamine-phalloidin (Invitrogen, #R415) for 30 min prior to microscopy acquisition. Immunofluorescence Following the fixation procedure, the cells were permeabilized using saponin diluted to 0. 025% in PBS for 20 min at room temperature. Next, 3 PBS washes were performed and blocking was done with 2% Bovine Serum Albumin (Sigma Aldrich,#A7906-100G) and 5% goat serum (Sigma Aldrich, #G9023) in PBS for 1 hour at room temperature. Afterwards, rabbit anti-SNX8 primary antibody (Sigma Aldrich, #HPA057296) diluted at 1:250 in blocking solution was incubated 1h at room temperature followed by a 45min incubation of a goat anti-rabbit Alexa-488 secondary antibody (Invitrogen, #A11034) diluted to 1:500 together with Hoechst and rhodamine-phalloidin.
Glioblastoma Stem-like Cells To investigate the molecular basis for MALT1 control of the endo-lysosome fitness 1,2 , a dual approach of RNAseq and proteome-wide label-free quantification (LFQ) analysis was conducted in patient-derived GSCs treated with the MALT1 inhibitor mepazine (MPZ)  53, 54  (Figure  1A ). The terms ''cholesterol biosynthesis'', ''regulation of cholesterol biosynthesis by SREBP'', and ''metabolism of steroids'' were among the top up-regulated pathways identified within the RNAseq dataset (Figure  1B , Gene Expression Omnibus GSE139018). This strong enrichment of mRNA encoding cholesterol-related genes was further visualized by gene set enrichment analysis (GSEA) of the reactome ''cholesterol biosynthesis'' and the "Horton Sterol Responsive Element Binding Factor targets'' (Figure  1B ).
Most of the enzymes involved in de novo cholesterol biosynthesis are under the control of the SREBP2 transcription factor 57 (Figure  1B ). Interestingly, MALT1 inhibition with mepazine and MLT748 led to a two-fold increase in the SREBP2 promoter activity (Figure  1G ). This was accompanied by an increase, both at the mRNA and the protein levels of LDLR, the main entry road for extracellular cholesterol, and a canonical SREBP2 target  34  , in three patient-derived cell lines (Figure  1H-I ). Furthermore, in silico analysis of the TCGA database using the Gliovis  58  tool uncovered the correlation between the SREBP2 level of expression and the probability of survival in GB patients (Figure  S1A ). In fact, SREBP2 appeared significantly less expressed in GB samples. Of note, this was not the case for SREBP1 (Figure  S1B ), which operates in parallel with SREBP2 to regulate additional processes, such as lipid droplet generation and fatty acid biosynthesis  57.
NPC1 Blockade Partially Recapitulated MALT1-repressed Phenotypes in GSCs We then tested whether NPC1 dilution from lysosomes could execute MALT1-related cell death in GSCs. In silico analysis of the TGCA database displayed that a low level of NPC1 RNA expression correlated with a significantly higher probability of survival in GB patients (Figure  5A ). This was however not the case for NPC2 (Figure  S5A ). As expected, the use of U18666A, a classical NPC1 inhibitor  65  , promoted SREBP2 processing and LDLR expression  65  (Figure  5B ), paralleling the effects of MALT1 hindrance (Figure  1I ). Likewise, the SREBP2 reporter assay supported the hypothesis of the activation of SREBP2 upon NPC1 blockade in GSCs (Figure  5C ). Moreover, the filipin III staining was globally increased in NPC1-silenced GSCs (Figure  5D ).
TCGA and Single Cell RNAseq Database Analysis The Cancer Genome Atlas (TCGA) was interrogated using the Gliovis Platform (http://gliovis. bioinfo. cnio. es)  58. RNAseq database was used to investigate data related to SREBP2 and SREBP1 (155 patients) (RNA expression, probability of survival). Agilent 4502A database was used to investigate data related to NPC1 (488 patients) and NPC2 (416 patients) (RNA expression, probability of survival). All subtypes were included. For single cell RNAseq analysis of GB samples, the UCSC Cell Browser was used (https://cells. ucsc. edu)  88. The glioblastoma infiltrating vs. tumor core dataset  9  was inquired for SREBP1 and SREBP2 expression.
ACKNOWLEDGMENTS. We are grateful to past and present SOAP team members ( Nantes Université, INSERM, CNRS, France ), especially  Agniezska Barbach  and  Lucas Ottero. We also thank  Laetitia Durand  and  François Paris  ( Nantes Université, INSERM, CNRS, France ). We also thank  Cédric Broussard ,  Johanna Bruce ,  Philippe Chafey , and  François Guillonneau , from  Plateforme Proteom'IC 3P5, Université de Paris, Institut Cochin, Paris, France  for performing sample preparation, acquisition, and analysis. We would like to acknowledge the core-facilities from  UMS Biocore, Nantes, France  (MicroPICell  ANR-10-INBS-04  and Cytocell). FINANCIAL SUPPORT. This work was supported by  Fondation ARC contre le Cancer, INCa PLBIO  ( 2019-151 ,  2019-291 ,  INCa PAIR-CEREB lNCa_16285 ),  Ligue Nationale Contre le Cancer  ( EL2022 ) and  Comités Ligue 35 ,  44 ,  49 ,  72 ,  85 , and  Region Pays de la Loire.
( rank in ordered dataset (*100) rank in ordered dataset (*100) not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint this version posted February 28, 2023. ; https://doi. org/10. 1101/2023. 02. 27. not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint this version posted February 28, 2023. ; https://doi. org/10. 1101/2023. 02. 27. not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint this version posted February 28, 2023. ; https://doi. org/10. 1101/2023. 02. 27. which was not certified by peer review) is the author/funder.
6. 6. 0 90. The database used was a Human sequence from the Uniprot databases (release March 2020). The enzyme specificity was that for trypsin. The cleavage specificity was trypsin's with maximum 2 missed cleavages. Carbamidomethylation of cysteines was set as constant modification, whereas acetylation of the protein N terminus and oxidation of methionines were set as variable modifications. The false discovery rate was kept below 1% on both peptides and proteins. LFQ was performed using both unique and razor peptides. At least two such peptides were required for LFQ. The "match between runs" (MBR) option was allowed with a match time window of 0. 7min and an alignment time window of 20min. For differential analysis, LFQ results from MaxQuant were imported into Perseus software version 1.
Funding Funding for CM doctoral thesis is provided by the  Q3 Ministry of Education and Research (MESR)  and her work is supported by the  Villa M foundation. CEM received financial support from the  ANR Corofet  grant ( ANR-21-CE14-0030 ) and  D efi Cl e Cell Based Biotherapies Occitanie (EXOBARRIER). Our team received institutional grants from  INSERM ,  CNRS  and  Toulouse III University.
A C T Post-transplantation cyclophosphamide (PTCy) has emerged as a promising approach for preventing graft-versus-host disease (GVHD) in allogeneic hematopoietic stem cell transplantation (allo-HSCT). However, there is a lack of studies examining the impact of this GVHD prophylaxis when different donor types are used in patients with Hodgkin lymphoma (HL). This study compared the outcomes of patients with HL undergoing HSCT from HLA-matched donors, including matched sibling donors (MSDs) and matched unrelated donors (MUDs), and haploidentical donors, using PTCy as the GVHD prophylaxis approach in all cohorts. We retrospectively compared outcomes of allo-HSCT from 166 HLA-matched donors (96 sibling and 70 unrelated donors) and 694 haploidentical donors using PTCybased GVHD prophylaxis in patients with HL registered in the European Society for Blood and Marrow Transplantation database from 2010 to 2020.
Inclusion Criteria All adult patients (age 18 years) patients diagnosed with HL who underwent their first HSCT between January 2010 and December 2020 from an MSD, MUD, or haploidentical donor and whose data were reported to the EBMT registry were included. For the purpose of this study, only HSCT recipients who received PTCy-based GVHD prophylaxis were considered. HLA typing was determined at high-level resolution for 10 loci, including HLA-A, -B, -C, -DRB1, and -DQB1. Full MSDs and MUDs were defined as 10/10 allelematched. The study excluded patients who received cord blood or ex vivo graft manipulation.
To overcome this limitation, we combined MSD and MUD transplants as HLA-matched HSCT to expand the data pool. To ensure robust and unbiased results, within this group we initially confirmed the absence of clinically significant differences in transplantation outcomes between recipients of MSD and MUD allografts. In addition, our study revealed differences associated with donor type, including higher proportions of patients with prior autologous HSCT, TBIbased conditioning, and more immunosuppressive drugs in addition to PTCy and decreased use of peripheral blood stem cell grafts in the haploidentical cohort compared to the HLA-matched group. Despite these differences in transplantation approaches, we conducted a comprehensive analysis of transplantation outcomes (including GVHD, NRM, PFS, and OS) comparing HLAmatched HSCT and haploidentical HSCT across the entire dataset.
To minimize potential biases, we      further analyzed transplantation outcomes by restricting comparisons to patients receiving peripheral blood stem cell grafts and those receiving non-TBI-containing conditioning regimens. These more homogeneous subanalyses predominantly confirmed key findings observed in the overall dataset. Nevertheless, these findings provide compelling evidence of significant differences in transplantation outcomes attributed to donor type when using PTCy-based GVHD prophylaxis. Consistent with previous studies  [15, 23, 24] , our findings indicate a significant delay in and a lower rate of platelet engraftment in the haploidentical HSCT cohort but no significant differences in neutrophil engraftment between the 2 cohorts. This observation could be attributed to the higher use of bone marrow grafts and likely a greater use of potentially myelotoxic antiviral drugs in the haploidentical group.
B-cell repertoires are characterized by a diverse set of receptors of distinct specificities generated through two processes of somatic diversification: V(D)J recombination and somatic hypermutations. B cell clonal families stem from the same V(D)J recombination event, but differ in their hypermutations. Clonal families identification is key to understanding B-cell repertoire function, evolution and dynamics. We present HILARy (High-precision Inference of Lineages in Antibody Repertoires), an efficient, fast and precise method to identify clonal families from high-throughput sequencing datasets. HILARy combines probabilistic models that capture the receptor generation and selection statistics with adapted clustering methods to achieve consistently high inference accuracy. It automatically leverages the phylogenetic signal of shared mutations in difficult repertoire subsets.
Disease-specific cohorts are now routinely subject to repertoire sequencing studies, which help to quantify and understand the dynamics of the B-cell response  [9, 10]. Partitioning BCR repertoire sequence datasets into clonal families is a critical step in understanding the architecture of each sample and interpreting the results. Identifying these lineages allows for quantifying selection  [11] [12] [13]  and for detecting changes in longitudinal measurements  [10, 14]. In recent years, many strategies have been developed that take advantage of CDR3 hypervariability  [15] : it is generally unlikely that the same or a similar CDR3 sequence be generated independently multiple times  [13, 16]. Other approaches make use of the information encoded in the intra-lineage patterns of divergence due to mutations  [17, 18].
A. Analysis of pairwise distances within VJl classes A common strategy for partitioning a BCR repertoire dataset into clonal families is to go through all pairs of sequences and identify pairs of clonally related sequences. In the following, we call such related pairs positive, and pairs of sequences belonging to different families negative. Then, the partition is built by single-linkage clustering, which consists in recursively grouping all positive pairs. Two characteristics of the repertoire complicate the search for this partition: large total number of pairs and low proportion of positive pairs, called prevalence and denoted by ρ. Low prevalence is usually due to a high frequency of singletons, which are sequences with no relative in the dataset. A pair of related sequences is expected to share the same V and J gene usage, as well as the same CDR3 length l, determined by alignment to the templates (Figure  1A ).
For a description of the data preprocessing and alignment to the V and J gene templates see Methods section IV A. The methods developed here begin by partitioning the data into VJl classes, defined as subsets of sequences with the same V and J gene usage, and CDR3 length l (Fig. 1B ). Clonal relationships are then found within these classes. While this severely limits the number of unnecessary comparisons, some VJl classes still exceed 10 5 sequences in large datasets, leading to the order of 10 10 pairs (see Figure  1C  for the distribution of the VJl class sizes N for donor 326651 of  [1] ). The signature of the VDJ rearrangement is largely encoded by the CDR3 alone. As we will see, CDR3 length has a strong impact on the difficulty of clonal family reconstruction. The distribution of CDR3 lengths l observed in the data is shown in Fig.
1D. In what follows we focus primarily on productive sequences, defined by their CDR3 length being a multiple of 3, which dominate most sequencing datasets. In addition, we restrict our analysis to CDR3 lengths between 15 and 105: shorter or longer junctions have comparable frequencies to nonproductive junctions, suggesting that they are nonfunctional. Within each VJl class, we compute the Hamming distance n of each pair of CDR3s, defined as the number of positions at which the two nucleotide sequences differ. The distribution of these distances normalized by the CDR3 length, denoted by x, shows a clear bimodal structure, with two identifiable components (Fig. 2A ): the contribution of positive pairs (of proportion ρ) peaks near x = 0 and decays quickly, whereas the bell-shaped contribution of negative pairs (of proportion 1ρ) peaks around x = 1/2.
C. Tests on synthetic datasets So far we have presented a method to set a highprecision threshold with predictable sensitivity, based on estimates from the distribution of distances P (x) only. To verify that these performance predictions hold in real inference tasks, we designed a method to generate realistic synthetic datasets where the clonal family structure is known. This generative method will also be used in the next sections to create a benchmark for comparing different clustering algorithms. Briefly, we first estimated the distribution of clonal family sizes from the data of  [1]  by applying the CDR3based clustering method with adaptive threshold described above to VJl classes for which the inference was highly reliable, i. e. for which the predicted sensitivity was ŝ(t * ) ≥ 90%.
In that limit, clusters are clearly separated and the partition should depend only weakly on the choice of clustering method. The resulting distribution of clone sizes follows a power-law with exponent -2. 3. Then, for each lineage, we draw a random progenitor using the soNNia model for IgH generation (Fig. S4 ), as well as a random size from the power-law distribution. Mutations are then randomly drawn on each sequence of the lineage in a way that preserves the mutation sharing patterns observed in families of comparable size from the partitioned data (Fig. S5 ). We thus generated 10 4 lineages and 2. 5 • 10 4 sequences. More details about the procedure are given in the Methods, section IV E. We applied the CDR3-based clustering method to this synthetic dataset. The sensitivity achieved at t * roughly follows and sometimes even outperforms the predicted one ŝ(t * ) across different values of ρ and l (Fig.
The standard deviations are likewise defined as σ 1 (n) ≡ n 2 1 -n 2 1 = (1/L) l(l + L)(n L + 1) and σ 0 (n 0 ) = n 2 0 0 -n 0 2 0 = n 1 n 2 /L (Methods, section IV F). For more than 99% of positive pairs, both sequences are mutated, i. e. n 1 , n 2 > 0 (Fig. 3B ). Without loss of sensitivity, we focus on the mutated part of the dataset, since we cannot use y for non-mutated sequences. The distributions of x and y for positive and negative pairs (Figs. 3C  and D ) are well separated, with positive pairs characterized by an overrepresentation of shared mutations. By adding the phylogenetic signal y we can identify positive pairs of sequences that have significantly diverged in their CDR3 (x > 0) but share significantly more mutations than expected (large y). Computing y for each pair of sequences is computationally expansive.
E. Benchmark of the methods We compare our approach to state-of-the-art methods, using the synthetic data described above as ground truth. In addition to our two algorithms-the CDR3-based method and the full method using both CDR3 and shared mutations-our benchmark includes the alignment-free method of  [20] , Partis  [21] , and Scoper  [22]. First, we measure the inference time of each algorithm. We find that the inference time is primarily affected by the size of the largest VJl class. Therefore, we measure the inference time using the largest class found in donor 326651 of dataset  [1]  with the size of N = 1. 2×10 5 unique sequences. We then apply the methods to a series of subsamples of this class to get the computational time as a function of the subsample size (Fig. 4A ). We only allowed for runtimes below 1 hour.
We find that only 3 methods achieve satisfactory performance: the two methods introduced here, and the alignment-free method. The other two methods, Scoper and Partis, are limited to VJl classes of small size (< 10 3 ). To compare the five algorithms in finite time, we test the accuracy of the methods using synthetic datasets with different CDR3 lengths. We focus on short CDR3s, l ∈  [15, 45] , which are the most challenging for lineage inference. Each dataset contains 10 4 unique sequences, so that the dominant VJl class is typically of size ∼ 10 3 and can be handled by all 5 algorithms. We measure performance using three metrics applied to the resulting partition: pairwise sensitivity s post (Fig. 4B ), pairwise precision π post (Fig. 4C ), and the variation of information v (Fig. 4D ). Pairwise sensitivity s post and precision π post are a posteriori analogs of the a priori estimates defined before in Eqs.
F. Inference of clonal families in a healthy repertoire We next use our method to infer the clonal families of the heavy-chain IgG repertoires of healthy donors from  [1]. Fig. 5  summarizes key properties of the inferred clonal families of donor 326651. We take advantage of the consistency of our method across CDR3 lengths, as evidenced by the benchmark, to study how the lineage structure changes with the CDR3 length. To this end, we divide the dataset into 9 quantiles, each containing between 8 and 12% of the total number of sequences (Fig. 5A, inset ). We find that across the 9 subsets of the data, the statistics of the lineage structure inferred with the mutationsbased method are largely universal. The distribution of the clonal family sizes z (Fig. 5A ) follows a power law across all CDR3 lengths under study, with no significant differences between different lengths.
This results generalizes an earlier observation used above for generating synthetic datasets, but which was restricted to high-ρ, high-l VJl classes, and justifies a posteriori the use of a universal power law in the generative model. For the largest families, of size z ≥ 100, we compute two intra-lineage summary statistics: the site frequency spectrum, which gives the distribution of frequencies of Inference results for donor 326651 of  [1]  are presented for 9-quantiles of the CDR3 distribution, each containing between 8 and 12% of the total number of sequences (corresponding to 9 colors in the inset of A). (A) Distributions of family size z. All CDR3 length quantiles exhibit universal power-law scaling with exponent -2. 3 (B) Site frequency spectra estimated for families of sizes z = 100.