dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction For patients with relapsed or refractory large B-cell lymphoma (LBCL), platinum-based immunochemotherapy followed by high-dose chemotherapy and autologous stem cell transplantation (ASCT) in patients who are sensitive to chemotherapy has been the standard of care (SOC) for decades. 1, 2 [9]  Lisocabtagene maraleucel (liso-cel) is an autologous, CD19directed, 4-1BB costimulated CAR T-cell product administered at equal target doses of CD8 + and CD4 + CAR + T cells. Results of a prespecified interim analysis of the TRANSFORM study (www. clinicaltrials. gov, #NCT03575351), performed at a median follow-up of 6. 2 months, demonstrated superior efficacy of liso-cel than that of SOC as a second-line treatment for patients with LBCL primary refractory to or relapsed within 12 months of Regular Article first-line therapy; the overall survival (OS) data were immature.
Cloning, expression, and purification A construct containing the full-length HTNV-L gene (strain 76-118/ Korean hemorrhagic fever, GenBank: X55901. 1 UniProt: P23456) flanked in its N-terminus with a hexa-histidine tag was cloned in a pFastBac vector between NdeI and NotI restriction sites. A D97A mutation was introduced using the QuickChange II Site-Directed Mutagenesis method (Agilent). PCR primers were 5′-TTC AAA ATG ACC CCC GCT AAC TAC AAG ATC TCT GGC-3′ (forward) and 5′-GCC AGA GAT CTT GTA GTT AGC GGG GGT CAT TTT GAA-3′ (reverse). Reactions were performed with CloneAmp™ HiFi PCR Premix (Takara bio). To digest the parental and nonmutated dsDNA plasmid, the amplification reaction, previously analyzed by 1% agarose gel, was incubated with DpnI (NEB) at 37 °C for 1 h. The construct was sequenced (Genewiz) before further use.
Reactions were stopped by adding native loading buffer (0. 05 mM Bromophenol blue, 1 mM EDTA, 6% [v/v] glycerol) before separation on a 4% agarose gel. Native migration of RNA-Protein complex was performed at RT in 0. 5× TAE (40 mM Tris-acetate, 1 mM EDTA) at 100 V for 30 min. Gels were visualized with a Phosphorimager Typhoon system and analyzed with ImageQuant TL program (Amersham).
Cryo-EM grids were then immediately prepared. All conditions were frozen on UltraAuFoil 300 mesh, R1. 2/1. 3 grids (Quantifoil). The grids were glow-discharged at 25 mA during 45 s. 3. 5 μL of sample were deposited on the grid that was subsequently blotted for 3 s (blot force 1) at 100% humidity and 4 °C in a Vitrobot Mark IV (Thermo Fisher Scientific) before plunge-freezing in liquid ethane. Cryo-EM data collections were performed on a 200 kV Glacios cryo-TEM microscope (Thermo Fisher Scientific) equipped with a K2 summit direct electron detector (Gatan). Coma and astigmatism correction were performed on a carbon grid. Automated multi-holes (3 × 3) data collection was performed with SerialEM  29. Movies containing 50 frames were acquired with a defocus between -0. 8 μm and -2. 2 μm at a nominal magnification of ×36,000 with pixel size of 1.
Image processing Equivalent image processing strategy was used for all the datasets. Movies were imported in Relion 4. 0 in nine optic groups based the nine different beam shifts required for their acquisition. They were realigned using Motioncor2 30 by applying the gain reference and the camera defect corrections. The micrographs were imported into cryoSPARC 3. 3. 2  31 for the following steps. CTF parameters were determined using the "Patch CTF estimation (multi)" tool on the nondose-weighted micrographs. Low-quality micrographs were manually removed thanks to the "Manually Curate Exposure" tool (Supplementary Figs. 2, 9, 11). The selected micrographs were subjected to an automated picking with the "Blob picker tool" designed to select particles with a diameter comprised between 100 and 190 Å.
Acknowledgements We thank  Guy Schoehn  for setting up the IBS/ISBG EM platform and for discussion;  Eleftherios Zarkadas  for support and technical advice on cryo-EM data collection;  Martin Pelosse  for technical advice on expression;  Aymeric Peuch  for setting up and maintaining the EM computing cluster;  Barbara Selisko  and  Delphine Baud  for management of the radioactivity facilities;  Dominique Housset ,  Allison Ballandras-Colas ,  Florian Chenavier ,  Stephen Cusack , and  Thibaut Crépin  for useful discussions;  Claire Debarnot ,  Daphna Fenel , and  Madalen Le Gorrec  for technical support;  Mackensie Thompson  for English proofreading. This work used the platforms of the Grenoble Instruct-ERIC center (ISBG;  UAR 3518 CNRS-CEA-UGA-EMBL ) within the  Grenoble Partnership for Structural Biology (PSB)  and the Marseille platform for integrative structural biology (AFMB-PBSIM platform), both supported by  FRISBI  ( ANR-10-INBS-05-02 ).
The ISBG; UAR 3518 was also supported by  GRAL , financed within the  University Grenoble Alpes graduate school (Ecoles Universitaires de Recherche) CBH-EUR-GS  ( ANR-17-EURE-0003 ). The electron microscope facility is supported by the  Auvergne-Rhône-Alpes Region , the  Fondation pour la Recherche Médicale (FRM) , the fonds  FEDER  and the  GIS-Infrastructures en Biologie Santé et Agronomie (IBiSA). We acknowledge the  European Synchrotron Radiation Facility  for the provision of beam time on CM01 and we would like to thank  Grégory Effantin  for their assistance. We thank the platform staff that enabled us to perform these analyses. IBS acknowledges integration into the  Interdisciplinary Research Institute of Grenoble (IRIG, CEA ). This work was supported by the  ANR-19-CE11-0024  to H.
Data availability The coordinates and structure factor generated in this study have been deposited in the Protein Data Bank and the Electron Microscopy Data Bank database under accession codes: 8C4S, EMD-16427 (HTNV-L apo core), 8C4T, EMD-16428 (5′vRNA-bound HTNV-L), 8C4U, EMD-16429 (HTNV-L in pre-initiation state), 8C4V, EMD-16430 (HTNV-L in elongation state). Source data are provided with this paper. Access to the "minimum dataset" that is necessary to interpret, verify and extend the research in the article can be made available through request to H. M. and J. R. Source data are provided with this paper.
Fifteen-month rates were 71. 5%, 50. 9% and 56. 7%, respectively. Common adverse events (any grade; grade 3-4) included infections (69. 9%, 39. 8%), cytokine release syndrome (57. 7%, 0%), anemia (48. 8%, 37. 4%), and neutropenia (48. 8%, 48. 8%). With biweekly dosing, grade 3-4 adverse events decreased from 58. 6% to 46. 6%. Elranatamab induced deep and durable responses with a manageable safety profile. Switching to biweekly dosing may improve long-term safety without compromising efficacy. ClinicalTrials. gov identifier: NCT04649359. The introduction of immunomodulatory drugs, proteasome inhibitors and anti-CD38 monoclonal antibodies has transformed the treatment landscape in multiple myeloma. The addition of these agents has substantially improved patient survival; however, outcomes for patients with disease progression after these agents remain poor with a median progression-free survival (PFS) of 4.
Elranatamab (PF-06863135) is a humanized bispecific antibody that targets both BCMA (on myeloma cells) and CD3 (on T cells)  3. Elranatamab activates and directs T cells to induce a cytotoxic T-cell response against myeloma cells  10. Preliminary data from the ongoing phase 1 MagnetisMM-1 study (NCT03269136) demonstrated encouraging safety and efficacy of elranatamab in patients with relapsed or refractory multiple myeloma  [11] [12] [13] [14]. The registrational phase 2 MagnetisMM-3 study (NCT04649359) evaluated the efficacy and safety of elranatamab monotherapy in patients with relapsed or refractory multiple myeloma  15, 16. Results in patients without prior BCMA-targeted treatment (cohort A) after ~15 months of follow-up, including clinical experience in patients who switched to biweekly dosing after persistent response, are reported.
For example, anti-GPRC5D-targeted agents have been associated with specific toxicities such as dysgeusia and skin and nail toxicity  26. The selection of targeted therapies for patients with relapsed or refractory multiple myeloma following treatment with immunomodulatory drugs, proteasome inhibitors and anti-CD38 antibodies will depend on disease characteristics and patient-related factors such as comorbidities and toxicities with prior treatments. The interpretation of the results in this study is limited by its single-arm design and lack of direct comparison with other treatment options, as well as by the small sample size in some subgroups. Longer-term follow-up is required to confirm benefits in DOR, PFS and OS. However, current results support further investigation of elranatamab. An ongoing open-label, randomized phase 3 study is evaluating elranatamab monotherapy versus elranatamab + daratumumab versus standard of care daratumumab + pomalidomide + dexamethasone in patients with relapsed or refractory multiple myeloma who have received lenalidomide and a proteasome inhibitor (NCT05020236).
DOR (for patients with confirmed objective responses) was the time from the first confirmed response to confirmed PD or death due to any cause, whichever was earlier, or censoring. PFS was the time from the first dose to confirmed PD or death due to any cause, whichever was earlier, or censoring. For DOR and PFS, patients were censored at the last valid assessment before (1) initiation of new anticancer or (2) two consecutive missed efficacy assessments before an event. OS was the time from the first dose to death due to any cause or censoring. Patients not known to have died were censored at the last contact date. A prespecified sensitivity analysis evaluated response by a computerized algorithm. Responses were derived per IMWG based on the local laboratory and bone marrow data and the individual lesion data provided by the investigator.
Acknowledgements This study was funded by  Pfizer. The authors would like to thank the patients who participated in the trial and the medical staff of participating centers. The medical writing support was provided by  G. Shay-Lowell, Ph. D. , CMPP, of  Engage Scientific Solutions  and was funded by  Pfizer. Therapeutics  and Arcellx; personal fees from  ITeos Therapeutics ,  Janssen ,  Legend Biotech ,  Pfizer ,  Sanofi  and  Trillium Therapeutics ; institutional research support from  Bristol-Myers Squibb ,  Genentech ,  Janssen Oncology ,  Pfizer ,  Sanofi ,  Trillium Therapeutics  and patents, royalties and/or other intellectual property interests with Serametrix. M. H. T. reports consulting or advisory roles for Janssen. G. K. , Y. J. , A. E. G. and D. A. S. have no competing interests to report.
Methods The ANRS CLAPT project consisted in two complementary quantitative and qualitative studies among PWH and PrEP users. The qualitative study have already been published elsewhere  [18] [19] [20]. The quantitative study consisted in one self-administered questionnaire among PWH and PrEP users in three Hospitals in France (Hôtel-Dieu University Hospital, Paris; Saint-Louis University Hospital, Paris, and Melun Hospital, Melun). Data collected from participants included lifestyle issues, medical history, treatment experience, perceived benefits and inconveniences of LAA. Not fully aware of the future approved indication for LAA at the time of the study, no particular drug was mentioned. The objective of this research was rather to describe individuals' perceptions in terms of a new route of drug administration and to assess its acceptability.
Targeted active screening was also performed at villages previously identified as being at greatest risk in a given area based on historical, epidemiological and geographical data as defined by the so-called "identification of villages at risk" (IVR) strategy  [19]. This strategy was mainly applied in the historical HDs. During IVR activities, clinical and epidemiological suspects were tested with a simplified algorithm (Fig  2B ). Serology was based on CATTb or a rapid diagnostic test (RDT SD1, Abbott Diagnostics, South Korea)  [20]  and parasitology performed on CATTb or RDT positive subjects, was based only on LNA if enlarged lymph nodes were present. For CATTb or RDT positive subjects, a sample of blood dried on filter paper was collected to perform TL. In case of LNA-positive subjects (confirmed gHAT cases) or when the TL was positive on at least one LNA-negative subjects, the village concerned was automatically selected for exhaustive active screening.
Staging was performed on confirmed parasitological cases. TL on blood dried on filter paper  [24]  was then carried out on RDT-positive but parasitology-negative subjects. TL-positive subjects were considered TL-seropositive targeted for targeted active screening as described above. In May 2018, passive screening was also set up in 13 health centres in the hypo-endemic HDs. The 13 health centres were selected based on epidemiological data (geographical distribution of the last cases detected) and their areas of influence, and in five neurology or psychiatry services in Bingerville (1), Bouake(2) and Abidjan (2) (Fig  4 ) for national coverage. The diagnostic algorithm used was based on the RDT SD1 (Fig  3B ). The 18 sites were supervised every three months and capacity building for doctors, nurses and laboratory technicians was carried out every year to optimise the effectiveness of the gHAT monitoring programme.
Tsetse are explicitly included in the model to capture human-tsetse-human transmission cycles and, furthermore, it is assumed that there is differential risk in exposure to tsetse bites between different people. As described in the vector control section, from 2016 and 2017, vector control was implemented in Bonon (in BouafleHD) and Sinfra, respectively, and the corresponding reduction in tsetse populations is included in the transmission model for these foci. Tsetse trap data were used to inform the parameters associated with observed tsetse population reduction by fitting the tsetse population dynamic sub-model to these data via maximum likelihood estimation (see S1 Text). To fit the full deterministic epidemiological model to data we use a Markov chain Monte Carlo (MCMC) methodology (see S1 Text) which compares annual active and passive case reporting simulated in the model to those observed in the data for each year for each HD.
Acknowledgments The authors wish to thank their institutions, and all contributors to the control of gHAT in Cote d'Ivoire, whatever their status. The PNETHA and partners thank  WHO  for their support to collect and consolidate data on reported gHAT cases in the frame of the WHO Atlas. The authors wish to pay tribute to  Pierre Cattand  who significantly contributed to the HAT control in Cote d'Ivoire as head of the research laboratory as well as training within the  WHO Clinical Research  Project on Trypanosomiasis based in Daloa.
Genome annotation of Coxiella-LE from O. maritimus (named strain CLEOmar) and D. marginatus (strain CLEDm) was performed by running a DIYA v1. 0 custom pipeline  (Stewart, Osborne, and Read, 2009) , as described in  Ellegaard, Klasson, Näslund, Bourtzis, and Andersson (2013). Briefly, the DIYA pipeline included an initial gene calling step using Prodigal  (Hyatt, GL Chen, Lo-Cascio, Land, Larimer, and Hauser, 2010) , followed by tRNA and rRNA prediction using tRNAscan-SE  (Lowe and Eddy, 1997)  and RNAmmer  (Lagesen, Hallin, Rødland, Staerfeldt, Rognes, and Ussery, 2007) , respectively. Pseudogene prediction was performed by GenePrimp  (Pati, Ivanova, Mikhailova, Ovchinnikova, Hooper, Lykidis, and Kyrpides, 2010). Potential functions of predicted protein-encoding genes were assigned using BLASTp  (Camacho, Coulouris, Avagyan, Ma, Papadopoulos, Bealer, and Madden, 2009)  against the Uniprot database (The UniProt Consortium, 2012) and PfamScan with the PFAM database  (Punta et al.
, 2012). Manual curation was conducted using Artemis  (Rutherford, Parkhill, Crook, Horsnell, Rice, Rajandream, and Barrell, 2000). Insertion Sequences (hereafter IS) were predicted with ISsaga  (Varani, Siguier, Gourbeyre, Charneau, and Chandler, 2011). CLEOmar IS copy numbers were estimated by mapping Illumina reads with Bowtie2 v2. 4. 2 (--very-sensitive-local preset)  (Langmead and Salzberg, 2012)  against a database containing a reference copy for each IS and five single copy housekeeping genes (Table  S2 ). Coverage and associated descriptive statistics were calculated with Qualimap v2. 2. 1  (Okonechnikov, Conesa, and García-Alcalde, 2015). The relative copy numbers of IS elements were obtained using the average coverage of housekeeping genes as a reference. BUSCO v4. 0. 6 and the corresponding legionellales_odb10 database (creation date 24-04-2019) were used to assess genome completeness  (Seppey, Manni, and Zdobnov, 2019).
Comparative Genomics, Clusters of Orthologous Proteins Inference, and Phylogenomic Reconstruction The general functions of proteomes were assigned using BLASTp against the Clusters of Orthologous Groups (COG) database  (Tatusov et al. , 2003). The metabolic potential was assessed by using the proteomes as input for KAAS  (Moriya, Itoh, Okuda, Yoshizawa, and Kanehisa, 2007). Homology between CLEOmar pseudogenes and C. burnetii RSA 493 genes was assessed by a reciprocal best hit search strategy using MMseq2 (rbh --search-type 3 --max-seqs 100 --maxaccept 10)  (Steinegger and Söding, 2017). The core, shared, and specific Clusters of Orthologous Proteins (hereafter COPs) were inferred for 44 Coxiellaceae from all five genera: Coxiella (including four C. burnetii, nine Coxiella-LE, and 23 environmental Coxiella MAGs proteomes), Aquicella (two), Berkiella (two), Diplorickettsia (one), and Rickettsiella (three) (Table  S1 ).
Legionella pneumophila str. Philadelphia 1 was included as an outgroup since this bacterium belongs to the Legionellaceae, the sister family to the Coxiellaceae. Annotated Coxiellaceae genomes were downloaded from RefSeq. Gene calling of unannotated Coxiella MAGs was performed with Prokka v1. 14. 5 (--mincontiglen 200 --gram neg)  (Seemann, 2014). COPs were inferred with OrthoFinder v2. 3. 12 (-M msa -T iqtree)  (Emms and Kelly, 2019). Obtained COPs table was queried to retrieve specific subsets of COPs and to check for the presence/absence of COPs in different Coxiellaceae. UpSetR v1. 4. 0 package available in R v3. 6. 3 (R Core Team, 2020) was used to plot the different COPs intersections between genomes  (Conway, Lex, and Gehlenborg, 2017). Putatively horizontally transferred genes in C.
burnetii RSA493 were detected with HGTector v2. 03b (-m diamond --aln-method fast) (Q  Zhu, Kosoy, and Dittmar, 2014). A first species tree was obtained with OrthoFinder. In brief, 348 individually aligned COPs were selected by OrthoFinder to build a concatenated alignment. Then, the species tree of the Coxiellaceae dataset was inferred using the STAG algorithm  (Emms and Kelly, 2019). To obtain node support values, a second species tree was computed as follows: (i) positions with more than 50% of the sequences being gaps in the OrthoFinder concatenated alignment (107812 positions) were filtered out with Gblocks v0. 91b (21499 selected positions in 143 blocks)  (Castresana, 2000) ; (ii) IQ-TREE v2. 0. 3 was used to infer the Maximum Likelihood phylogenomic tree using the best suggested evolutionary model (-m MFP) and ultrafast bootstrap (-bb 1000) and SHlike approximate likelihood ratio test (-alrt 1000)  (Kalyaanamoorthy, Minh, Wong, Haeseler, and Jermiin, 2017; Nguyen, Schmidt, Haeseler, and Minh, 2015).
Single gene phylogenies were obtained by aligning homologous sequences with MAFFT v7. 310 (linsi algorithm)  (Katoh, Misawa, Kuma, and Miyata, 2002) , computing the Maximum Likelihood tree with IQ-TREE (same options as described above). FigTree v1. 4. 4 and InkScape v0. 92 were used respectively to plot and modify phylogenetic trees to their final version. A synteny plot of 458 single copy COPs shared between C. burnetii RSA493 and Coxiella symbionts of Amblyomma, Dermacentor, and Rhipicephalus tick species was generated with the genoPlotR v0. 8. 9 R package  (Guy, Kultima, and Andersson, 2010). A synteny plot of the Sha/Mrp antiporter and Dot/Icm T4SS region in selected Coxiella was produced with genoPlotR. Synteny between C. burnetii strains was computed using OrthoFinder  (Emms and Kelly, 2019).
IslandViewer 4 database was used to visualize and predict genomic (pathogenic) islands in Coxiella burnetii strains  (Bertelli, Laird, KP Williams, Lau, Hoad, Winsor, and Brinkman, 2017). geno-PlotR was used to plot synteny and the genomic location of the Sha/Mrp antiporter, the Dot/Icm T4SS region, and IslandViewer 4 results in C. burnetii strains. Figures were adjusted with InkScape.
C. burnetii Specialization: Acid Resistance and Biphasic Life Cycle C. burnetii is the only known acidophilic bacteria  (Hackstadt, 1983)  among Legionellales pathogens. It presents three amino acid Acid Resistance (AR) systems that work in a pH range of 4-6, close to that of the different Coxiella Containing Vacuole (CCV) phases  (Foster, 2004 ). The glutamate system (AR2) is the most effective  (Lund, Tramonti, and De Biase, 2014)  with optimal AR2 decarboxylase activity at pH 4, close to the CCV pH  (Foster, 2004). Interestingly, C. burnetii encodes two GadC-like transporters, CBU_1347 and CBU_2020. According to the TCDB classification engine, CBU_1347 presents the highest identity to GadC from Escherichia coli (GenBank: BAI30440. 1). In addition, CBU_1347 is up-regulated during the transition from SCV to LCV  (Sandoz, Popham, Beare, Sturdevant, Hansen, Nair, and RA Heinzen, 2016) , suggesting it may be the main AR2 antiporter.
Acknowledgments DSG was supported by the  European Union 's  Horizon 2020 research and innovation program  under a  Marie Skłodowska-Curie Individual Fellowship (GuardSym , grant agreement no. 885583 ). Preprint version 4 of this article was peer-reviewed and recommended by  Peer Community  in Microbiology (Tamarit, 2023). We would like to acknowledge the whole  PCI Community  for their effort and time, especially  Daniel Tamarit ,  Sophie Abby ,  Adam Ossowicki , and one anonymous reviewer for their constructive suggestions.
Funding This research was partially supported by ( 1) the projects  ANR Hmicmac 16-CE02-0014  to FV and  ANR MICROM 21-CE02-0002  to FV and OD granted by the  French National Research Agency (ANR) ; (2) the PHYLOTIQUE from the  RIVOC-KIM RIVE intiative of the University of Montpellier  and the  Exploratory grant MISTICKS  from the  LABEX CeMEB  ( Centre Mediterraneen de l'Environnement et de la Biodiversite ) to OD; (3) the  Exploratory grant DISTIC  from the  LABEX CeMEB  with the support an  ANR  " Investissements d'Avenir" program  ( ANR-10-LABX-04-01 ) to KM; (4) the  Israel Science Foundation  (ISF) # 1074/18  to YG; (5) the international grant EVOSYM co-managed by the  Ministry of Science, Technology and Space (Israel)  and the  French National Center of Scientific Research (CNRS, France)  to OD and YG; (6) the  LABEX ECOFECT  ( ANR-11-LABX-0048 ) of University Lyon 1, within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ), operated by  ANR ; (7) the " Investissements d'Avenir -Laboratoire d'Excellence" CEBA   ANR-10-LABX-25-01.
Diego Santos-Garcia et al. Peer Community Journal, Vol. 3 (2023), article e41 https://doi. org/10. 24072/pcjournal. 269 Data, Script, Code, and Supplementary Information Availability RAW reads generated for this work and Coxiella-LE CLEOmar (GCA_907164965) and CLEDm (GCA_907164955) genome assemblies are available European Nucleotide Archive (ENA) under the BioProject number PRJEB44453. Coxiella-LE CLEOmar and CLEDm annotated genomes, relevant performed analysis, scripts, and used data are available on FigShare (https://doi. org/ 10. 6084/m9. figshare. 12563558. v3, Santos-Garcia et al. , 2023). All phylogenetic trees can be accessed at https://itol. embl. de/shared/dsantosgarcia.