dataesr/scientific-paragraphs-categorization huggingface dataset:
Layouts of detailed networks in The correlation coefficient was calculated using the Pearson correlation coefficient (Supplementary Fig. 4 ). Enrichment of public CDR3s or virus-associated CDR3s was done using the two-tailed Chi-square test with Yate's correction (Fig. 2f , Supplementary table 1, 2 & 3) and the Fisher test (Supplementary Fig. 8 , Supplementary Fig. 11 ). Statistical comparisons and multivariate analyses were performed using Prism (GraphPad Software, La Jolla, CA) and using R software version 3. 5. 0 (www. r-project. org). PCA was performed on the frequency of VJ combination usage frequency within each donor using the factoextra R package. Tsnes were generated using the binding scores of each cell across all the antigens present in the dataset, disregarding the HLA matching with the donor.
The function Rtsne of the homonymous R package  42  was applied with the perplexity parameter set to 10. Correlograms were generated using the cells that have a significant binding score (i. e. >10) for at least one of the virus-specific dextramers tested. The correlation was calculated across all the antigens present on each correlogram (Pearson test). Correlograms were plotted using the corrplot R package.
Probability of generation calculation The generation probability (Pgen) of a sequence is inferred using the Olga 33 algorithm, which is inferred by IGoR 44 , for Fig. 1e , Fig. 2c. IGoR uses out-of-frame sequence information to infer patient-dependent models of VDJ recombination, effectively bypassing selection. From these models, the probability of a given recombination scenario can be computed. The generation probability of a sequence is then obtained by summing over all the scenarios that are compatible with it. We also used OLGA to generate a random repertoire of 500,000 sequences for each a or b repertoire and 3 down-sampling (of unique sequences) to get control repertoire equal to the size of COVID-19 BAL dataset used in fig. 4c, 4d. The control repertoire was parametrized by the predefined genomic templates provided with the package.
CDR3 connections between individuals In Supplementary Fig. 9 , the top 1,500 bCDR3s were sampled from each of the 12 datasets of DPCD3 + and CD8 + , then merged to obtain two datasets of 18,000 bCDR3s for DPCD3 + and CD8 +. We generated and represented networks, as described above, to investigate the bCDR3 interindividual network structure. Virus-specific bCDR3 tetramer public databases The virus-associated CDR3 databases used for the search for specificity was compiled from the most complete previously published McPAS-TCR  14  and VDJdb  15  databases. Virus-associated bCDR3s were selected from the original datasets only when derived from a TCR of sorted CD8 T cells that were bound by a specific tetramer. A total of 5,437 such unique tetramer-associated bCDR3s were identified and used. Peptides used for tetramer sorting were from cytomegalovirus (CMV), Epstein-Barr virus (EBV), hepatitis C virus (HCV), herpes simplex virus 2 (HSV2), human immunodeficiency virus (HIV), influenza and yellow fever virus (YFV).
Sorted cells were stimulated for 6 hours with either nothing or 1 μg/mL of SIINFEKL ovalbumin peptide (OVA), NLVPMVATV cytomegalovirus pp65 peptide (CMV) or GLCTLVAML Epstein-Barr virus BMLF-1 peptide (Ozyme®). The positive control (Ctl PMA/Iono) was performed with 50 ng/mL phorbol myristate acetate (PMA) and 1 mM ionomycin. Intracellular IFN-γ production with an IFN-γ-FITC antibody (BD Pharmingen) was detected in the presence of Golgi-Plug (BD Pharmingen®) after fixation and permeabilisation (BD Cytofix/Cytoperm). Data were acquired using a Navios flow cytometer and analysed with Kaluza analysis software (Beckman Coulter).
Influenza vaccination protocol Peripheral blood was obtained before vaccination and on day 14 after vaccination. PBMCs were stained with antibodies anti-CD3 (AF700), anti-CD8 (FITC), CD45RA (PeCy7), CCR7 (BV421). Cells were sorted by fluorescent activated cell sorting (Becton Dickinson TM FACSAria II) with purity >95% to collect populations based on the following labelling: naïve CD8 were gated as CD3 + CD8 - CD45RA + CCR7 + and effector memory CD8 were gated as CD3 + CD8 -CD45RA -CCR7 -. RNA extraction, library preparation, sequencing and raw data processing were performed as described above. Identification of TCR enrichment after vaccination Homologous groups of TCRs that are specifically recruited during an antigen-specific response following yellow fever or influenza vaccination were sought using with the previously published algorithms ALICE  21  and TCRNET  22, 23  , respectively.
Single clusters of bCDR3s from CD8 + thymocytes comprise TCRs with different viral specificities. The tagged dots represent bCDR3s with known specificity according to the public database  14, 15. bCDR3 specific for unrelated virus epitopes can be found close to or even directly linked in the cluster. Supplementary table 2. Enrichment of virus-specific bCDR3s from databases  14, 15  in clustered CD8 + thymocytes. Contingency table for the Chi-square analysis performed with Yates' correction to test the null hypothesis of independence between the specificity of bCDR3s (Virus Tet + and Unknown specificities) vs the connection of bCDR3 ("clustered" and "dispersed") in all the CD8 + thymocytes from 12 donors. The results (p-value < 0. 0001) rejected the null hypothesis, thereby indicating the interdependency of the two variables.
Virus-specific CDR3 single-cell dextramer public datasetThis dataset contains single-cell alpha/beta TCRs from 160 914 CD8 + T cells isolated from peripheral blood mononuclear cells (PBMCs) from 4 healthy donors. Briefly, 30 dCODE™ Dextramer® reagents (Immudex®) with antigenic peptides derived from infectious diseases (9 from CMV, 12 from EBV, 1 for influenza, 1 for HTLV, 2 for HPV and 5 for HIV) were simultaneously used to mark cells. Each Dextramer® reagent included a distinct nucleic acid barcode. A panel of fluorescently labelled antibodies was used to sort pure Dextramer®positive cells within the CD8 + T cell population using an MA900 Multi-Application Cell Sorter (Sony Biotechnology) in a reaction mix containing RT Reagent Mix and Poly dT RT primers. The Chromium Single Cell V(D)J workflow generates single cell V(D)J and Dextramer® libraries from amplified DNA derived from Dextramer®-conjugated barcode oligonucleotides, which are bound to TCRs.
Chromium Single Cell V(D)J enriched libraries and cell surface protein libraries were quantified, normalised, and sequenced according to the user guide for Chromium Single Cell V(D)J reagent kits with feature barcoding technology for cell surface protein. We used this dataset to study the presence of multiple specificities in TCR and CDR3. There were 139,378 unambiguous TCRs (with only one α and one β chain). We set the threshold defining positive binding at UMI counts greater than 10 for any given dextramer. This identified 15,195 unique virus-specific TCRs with at least one binding. Single-cell sequencing of TCRs from bronchoalveolar lavages from COVID-19 patients.
Acknowledgements The authors would like to express their gratitude to the donors and their families who allowed the collection of samples for research under sad circumstances. The authors would also like to thank  Prof. Pascal Leprince ,  Dr. Guillaume Lebreton , and  Dr. Marina Rigolet  of the cardiac surgery team, and  Prof. Bruno Riou  and the graft coordination team, of the  Pitié-Salpêtrière hospital  for their contribution to sample collection. The authors thank the  UMR 8199 LIGAN-PM Genomics platform (Lille, France)  for sequencing. We thank  Thierry Mora  and  Aleksandra Walczak  of the  Ecole Normale Supérieure de Paris  for helpful discussion.
Analytical Instruments Toxicological screenings were achieved using ultra-high pressure liquid chromatography (LC, Vanquish™, Thermo Scientific, Illkirch, France) coupled with High-Resolution Q Exactive Focus™ mass spectrometry (Thermo Scientific, Illkirch, France) driven by Thermo™ Q Exactive Focus software (Version 4. 1). LC was achieved on an Phenyl Hexyl column (Accucore®, 100 × 2. 1 mm, 2. 6 µm) (ThermoFisher Scientific, Bremen, Germany) maintained at 40 • C. Flow rate was set at 0. 5 mL/min. The elution was based on a binary gradient system. Solvent A consisted of water with 2 mmol•L -1 ammonium formate and solvent B a methanol/acetonitrile mixture with 2 mmol ammonium formate  (50:50). Both solvents A and B contained 0. 1% formic acid. Eluent was maintained at 1% B for 1 min, increased to 99% B for 10 min, held at 99% B for 1.
5 min before returning to 1% B, and finally, held for 4 min. The ionization voltage was set at 3. 5 kV for positive ion mode and 2. 5 kV for negative ion mode; sheath gas and auxiliary gas were 35 and 15 arbitrary units, respectively. S-lens RF was set at level 60; and vaporizer temperature and capillary temperature were both set at 320 • C. Nitrogen was used for spray stabilization, collision-induced dissociation experiments in the HCD cell, and as the damping gas in the C-trap. AGC target was fixed to 1 × 10 5 for MS/MS experiments. Transient time was fixed to 120 and 50 milliseconds for full MS and MS/MS scans, respectively. Data were acquired in full-scan data-dependent MS2 (dd-MS2) mode. In this mode, a full-scan acquisition was performed at a resolution of 70,000 between m/z 100 and 1000, followed by the acquisition of MS/MS spectra of the three most intense ions.
Analysis The MS and fragmentation data acquired were processed using the Thermo Scientific Forensic TraceFinder™ software version 4. 1. This specific software performs a thorough interrogation of the database by utilizing its built-in database and the Lariboisière mass spectral library of 1560 compounds. Different parameters, such as retention times, fragment and isotope patterns, and library matching were used to detect and confirm the drugs and metabolites. In our study, the detection of compounds was assessed by a signal intensity above 1. 10 6 and confirmed by total concordance between the experimental and library retention times, complete fragments and isotopic patterns, and by cross-matching the values above 70%. The detection and confirmation of the 5 internal standards in each sample allowed us to validate the pre-analytical and analytical phases of our method.
In future perspective studies aimed at comparing the two cohorts it could be interesting to carefully plan the data collection in order to analyze variables that may depend on observation time, e. g. SPM or need for treatment, with a more appropriate time-to-event approach, thus minimizing any bias due to different follow-up lengths. According to retrospective studies including few and heterogenous series of patients  [27] [28] [29]  , treatment of LGLL still relies on immunosuppressive therapy, where MTX and CTX are used upfront, while CyA is generally reserved for relapsed/refractory patients  1, 2, 21. To date, only one published prospective trial evaluating the efficacy of immunosuppressive therapy in LGLL is available  30  , and one prospective and randomized trial comparing MTX and CTX as first line therapy in LGLL is currently ongoing (NCT01976182).
The novel allele HLA-A*11:443 differs from HLA-A*11:01:01:01 by one non-synonymous nucleotide substitution in exon 2. Genomic DNA was extracted from self-collected saliva (Oragene DNA kit) using the Quickgene semi-automated method (Quickgene DNA whole blood kit L on the dedicated isolation device QG-Mini8L, Kurabo Industries Ltd, Osaka, Japan) according to the suppliers' recommendations. The HLA typing was first performed using ALLType NGS 11 loci kit (One Lambda, Canoga Park, CA, United States) on a Miniseq platform (Illumina, San Diego, SA, United States). The fastq files were analysed thanks to dedicated software, that is, TypeStream Visual version 2. 0. 1 (One Lambda). The metrics were excellent for all loci and especially locus A with a minimal depth of 1010Â and an average depth of 1362Â for the second exon bearing the nucleotide substitution.
All other exons on locus A showed higher minimal and average depths. To confirm the novel allele, an single-allele specific sanger sequencing was then performed using Protrans S4 HLA-A kit (Protrans, Hockenheim, Germany) on an ABI 3130 XL (Applied BioSystems, Waltham, Massachusetts, MA, United States). The generated files were analysed using SeqScanner, version 2. 0 (Applied BioSystems) to assess the quality of raw data, then using SeqPilot software, version 5. 2. 0 (JSI medical systems GmBH, Ettenheim, Germany) for the sequence interpretation. Using both techniques, the novel allele presents a single non-synonymous nucleotide substitution in exon 2 compared to its closer match HLA-A*11:01:01:01. The nucleotide change occurs at position 200 of the CDS sequence where adenine (A) is replaced by guanine (G) compared to the reference allele HLA-A*11:01:01:01 (Figure  1 ).
This results in a codon change from CAG to CGG, therefore to an amino acid change at residue 43 where glutamine (Q) is replaced by arginine (R). The 43Q of the HLA-A*11:01 molecule is not known to be an eplet in the HLA epitope registry. 1 However, it is adjacent to the 44RM or 44RME eplets know to have a high exposition using the ElliPro Antibody Epitope Prediction. 2  The analysis of the localisation of this aminoacid and its antibody accessibility with the pHLA3D database 3 indicated that this amino acid is particularly exposed and may trigger allogeneic reactions. We were unable to perform serological typing using micro-lymphocyte cytotoxicity assay because volunteer stem cell donors only provide salivary sample from which DNA is extracted for molecular biology analyses. F I G U R E The nucleotide sequence of the exons 1-8 of this novel allele was submitted to the GenBank database (Accession No.
OQ174844) and to the IPD-IMGT/HLA Database 4 (Submission No. HWS10065101). The name HLA-A*11:443 has been officially assigned by the WHO Nomenclature Committee for Factors of the HLA System 5 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. Lists of such new names will be published in the following WHO Nomenclature Report.
Amplified bacterial 16S rDNA products were individually tagged with a unique 35-base barcode using the Nextera Index Kit (Illumina, San Diego, CA, USA), purified and sequenced on an Illumina MiSeq platform (GenSeq, Montpellier University). All bioinformatic analyses were conducted using the pipeline FROGS (https:// github. com/ geral dinep ascal/ FROGS) mainly as follows by  [56] , considering that the clustering was here performed with an aggregation distance value of 3 and the sequences' clusters were clustered together with a minimum identity value of 96% to define OTUs. To eliminate the possibility of contamination, we included ten DNA extraction controls and three amplification control representatives of the different pins, sterile water, buffers and kits used. To treat contaminants' OTUs, if the count of an OTU in the sum of controls was > 50% of its count in the sum of samples, the OTU was removed.
We obtained an average number of 19,053 bacterial 16S rDNA reads per tick specimen. According to the obtained abundance file and OTU assignation, a random subset of DNA templates with reads of bacteria of potential interest was further used for additional molecular typing and refined bacterial identification. These tick-borne bacteria, including Rickettsia sp. , Borrelia sp. , Ehrlichia sp. and Cryptoplasma sp. , were each genotyped using specific semi-nested PCR assays (Additional file 1: Table  S1 ). To prevent possible contamination, different parts of this process were physically separated from one another in entirely separate rooms. Sequence alignments were performed using CLUSTALW  [57] , implemented in the MEGA7 software  [58]. The GBLOCKS program with default parameters was used to remove poorly aligned positions and to obtain unambiguous sequence alignments  [59].
The evolutionary models most closely fitting the sequence data were determined using Akaike information criterion with MEGA7  [58]. Phylogenetic analyses were based on maximum likelihood (ML) approach. A ML heuristic search, using a starting tree obtained by neighbor joining, was conducted, and clade robustness was further assessed by bootstrap analysis using 1000 replicates in MEGA7  [58]. All novel nucleotide sequences were deposited in the GenBank nucleotide database (accession numbers OQ092409-OQ092416, OQ092427-OQ092429, OQ096007-OQ096015).
Data analysis The numbers of ticks, tick species and tick pathogens were determined in each sampling site. To compare the mean abundance of ticks or their pathogens between seasons, a t-test was performed. Differences between multiple groups like vegetation types were examined using a one-way analysis of variance. We fitted generalized linear models with a Poisson structure to investigate whether the temperature and relative humidity of the day and the vegetation type influenced the number of ticks  [60, 61]. Before fitting the model, we tested for multicollinearity between variables. All continuous variables were scaled with a mean of 0 and standard deviation of 1 (i. e. z-transformation). We confirmed that there were no major internal correlation problems based on the variance inflation factor (VIF) (maximum VIF < 1.
Theileria ornithorhynchi Theileria of Amblyomma tholloni Theileria of Amblyomma tholloni Theileria of Amblyomma tholloni Theileria of Amblyomma tholloni Theileria of Amblyomma tholloni Theileria of Amblyomma tholloni Fig. 5  Piroplasmid phylogenetic tree constructed using maximum-likelihood (ML) estimations based on 18S rDNA nucleotide sequences (350 unambiguously aligned bp; best-fit approximation for the evolutionary model: T92 + G), including representative Babesia, Theileria, Hemolivia and Hepatozoon species (their GenBank accession numbers are indicated on the tree). Branch numbers indicate percentage bootstrap support (1000 replicates). Only bootstrap supports > 70% are shown. The scale bar is in units of substitution/site. Black squares indicate piroplasmid sequences obtained in this study discovery of A.
So, humans, by cutting the forest to clear a path, could have an impact on the distribution of ticks. Because ticks can have different feeding strategies, using multiple techniques wih different biases allowed us to collect a wider variety of tick species and life stages  [74, 75]. For example, flagging has been shown to favor larvae and nymphs while visual search favors adult ticks of Amblyomma sp. [75]. This could partially explain the high number of larvae and nymphs we collected in this study. Using human subjects favored ticks that feed on humans, while flagging targeted questing ticks with a broader spectrum of hosts  [74]. In that Fig. 6  Borrelia phylogenetic tree constructed using maximum-likelihood (ML) estimations based on flaB nucleotide sequences (438 unambiguously aligned bp; best-fit approximation for the evolutionary model: T92 + G), including representative Borrelia species (their GenBank accession numbers are indicated on the tree).
Acknowledgements We are grateful to the  Uganda Wildlife Authority ,  Uganda National Council for Science and Technology for permission  to conduct research at  Sebitoli in Kibale NP, Uganda. We offer profound thanks to  Jean-Michel Krief , co-director of the Great Apes Conservation Project, the field assistants of the Sebitoli Chimpanzee Project:  Deogratius Kiomuhangi ,  Emmanuel Balinda ,  John Koojo ,  Joseph Alinaitwe ,  Ibrahim Nyakana ,  Wilson Muzahura ,  Edward Kalyegira ,  Nelson Tugume , and especially  John Kakonge  and  Charles Twesige , for their valuable and cheerful fieldwork.
Materials White mulberry leaves (M. alba L. ) from Sakon Nakhon and Buriram cultivars were collected during May-June in Chiang Mai province (northern Thailand: 19 • 00 N, 99 • 00 E) under the supervision of the Queen Sirikit Department of Sericulture. The leaves were dried in an oven at 60 • C for 48-72 h and blended into powder with a blender. The samples were kept in airtight plastic bags for around 1-2 days prior to extraction. RAW 264. 7 macrophage cell line originated from mice (Mus musculus) and was purchased from ATCC with the accession number ATCC-TIB-71.
Acknowledgments: The authors would like to thank the  Thailand Science Research and Innovation (TSRI)  under the  Ministry of Higher Education, Science, Research and Innovation , the  Royal Golden Jubilee Ph. D. (RGJ Ph. D. ) Program, and Natural Extracts and Innovative Products for Alternative Healthcare Research Group, Chiang Mai University. The  Department of Biology, Faculty of Science , the  Graduate School, Chiang Mai University, Thailand , and  INRAE  ( Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement ), France are also acknowledged. We also would like to express our thanks to  Julia Akins  for her assistance in language editing.
We collected data from the health insurance databases before (2017) and after the implementation of the intervention (2019). The primary outcome was the prescription proportion of broad-spectrum antibiotics. Results: We included 42,956 Escherichia coliepositive urine cultures with AST. The decrease in the proportion of broad-spectrum antibiotic prescriptions between 2017 and 2019 was significantly higher for selective reporting of AST, attributable to a decrease in the prescription proportion of thirdgeneration cephalosporins (-8. 5% for selective reporting versus -0. 1% for complete reporting, p < 0. 001). This impact was more marked for targeted therapy and female patients. Requests from clinicians for the complete reporting of AST results were infrequent (1. 2% of all the selective AST results reported in 2019).
Data collection Data on each AST performed on E. coliepositive urine cultures during the 'before' period (2017) and the 'after' period (2019) in all laboratories of the two groups were collected from the laboratories' databases. An indirect data linkage method was conducted using the anonymous Health Insurance reimbursement databases on common variables. We then identified antibiotics prescribed in the 15 days following the urine culture and dispensed by a community pharmacy, and medical consultations and hospitalizations during the 30 days following the urine culture, for the corresponding patient. Antibiotics prescribed less than 3 days after the prescription of the urine cultures were categorized as empirical therapy, whereas antibiotics prescribed 3 days or more after the prescription of the urine cultures were categorized as targeted therapy.
Clinical indications are not available in these databases. During the 'after' period, biologists in the intervention group recorded all prescribers' requests for the complete reporting of AST results. Please see File S7 for details on the data collected and the indirect data linkage method. Selective reporting was implemented on 1 September 2018, we deliberately did not collect data for the year 2018, as this year was dedicated to the purchase and the configuration of the algorithmic software (January to August), and to its adjustment to the laboratory routine practice (September to December).
Group characteristics We included 21 123 E. coliepositive urine cultures with AST in 2017 and 21 833 in 2019. The matching rate following the data linkage method was 88. 4% in 2017 and 86. 5% in 2019 (see Table  S9  for details). As shown in Table  1 , the two groups were quite comparable in both study periods, with the only exceptions being the prevalence of E. coli in urine cultures and susceptibility of E. coli to cotrimoxazole and amoxicillin-clavulanate.