dataesr/scientific-paragraphs-categorization huggingface dataset:
QUANTIFICATION AND STATISTICAL ANALYSIS Data are presented as the mean values ± standard deviation (SD). The figure legends report the number of independent experiments. Statistical analysis was performed using R software environment for statistical computing and graphics, version 3. 3. 2. For levels of IFNα productions, viral RNA levels, viral titers and cell surface expression, the values were considered relative to untreated references for each independent experiment and analysis using an one-way ANOVA on ranks (Kruskal-Wallis test). When the test was significant, we used the Nemenyi post hoc test for multiple comparisons of mean rank sums to determine which contrast(s) between individual experimental condition pairs was significant. For the statistical analysis of polarization of structure/component at contact (assessed by confocal microscopy analysis), the distribution of the analyzed pDCs in contact with infected cells that presents co-clusters at the contact (without or with additional cluster outside contact) versus no co-cluster at contact (without or with cluster outside contact) were compared using Fisher's exact test for count data.
Similar results were obtained when using the test for equality of proportions. Statistical analysis of viral transmission from GFP -infected cells to GFP + uninfected cells was performed using one-way ANOVA, followed by Tukey multiple comparisons of means. The results of imaging flow cytometry (using Image Stream X technology) were analyzed using the Wilcoxon Unpaired test. For statistical analysis of the duration of contact (assessed by live-imaging using spinning-disk confocal microscopy analysis), a test for pairwise equality of proportions was applied to determine whether the duration of contact depends on the experimental conditions by comparing the frequency of the indicated time lengths. Data considered significant demonstrated adjusted p-value by False Discovery Rate (FDR) less than 0.
Acknowledgements We thank  A. D. Davidson  ( University of Bristol, UK ) for the molecular clone  NGC ,  A. Kohn  ( MRC-University of Glasgow Centre for Virus Research, UK ) and  R. Freitas de Oliveira Franca  ( FIOCRUZ, Recife, Brazil ) for clinical isolate ZIKV Brazil ( PE243_KX197192 ), the  European Virus Archive for H/PF ,  C. Rice  ( Rockeffeler University, New York, NY ) for the anti-HCV NS5A,  P. Despres  ( PIMIT, Université de La Réunion-INSERM France ) for providing the anti-DENV E,  F. V. Chisari  ( Scripps Research Institute, La Jolla, CA ) for the Huh7. 5. 1 cells and  V. Lotteau  ( CIRI, Lyon, France ) for  Influenza A Virus. We are grateful to  Y. Jaillais ,  H Paidassi ,  T. Walzer ,  S. Munoz-Gonzalez  for critical reading of the manuscript, and to our colleagues for encouragement and help.
The PLATIM platform of  SFR Biosciences Lyon-Gerland  ( UMS3444/US8 ) for imaging analysis and technical assistance. We acknowledge the contribution of the  EFS Confluence-Lyon. This work was supported by grants from the  'Agence Nationale pour la Recherche' (ANR-JCJC-EXAMIN) , the  'Agence Nationale pour la Recherche contre le SIDA et les Hépatites Virales'  ( ANRS-AO 2017-01 ), the  'Fondation pour le recherche médicale'  ( DBI20141231313 DREUX Bioinfo 2014 ) and  LabEx Ecofect  Grant  ANR-11-LABX-0048. SA's PhD fellowship was sponsored by French ministry and Ligue contre le cancer. The post-doc fellowship for BW and DC were sponsored by  EMBO  and  ANRS , respectively. The authors declare no competing interests. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Cytokine quantification Supernatants were collected after 24-hours of stimulation. Cytokine measurement was performed by Cytometric Bead Array Flex Set (BD Biosciences, Franklin Lakes, NJ). The following cytokines were measured in pDC supernatant: IL-6, IL-8, IFN-α, TNF-α, IP-10, and GZM-B, and for T cells: IL-2, IL-3, IL-4, IL-5, IL-6, IL-9, IL-10, IL-13, 1L-17A, IL-17F, GM-CSF, TNF-α, and IFN-γ. Acquisition was performed on a flow cytometer (BD LSR II), and data were analyzed using Fcap array (BD).
Funding: This work was supported by funding from  INSERM  ( BIO2014-08 ) (www. inserm. fr), Agence nationale de la recherche  (http://www. agence-nationale-recherche. fr/) ANR-13-BSV1-0024-02 ,  ANR-10-IDEX-0001-02 PSL �  and  ANR-11-LABX-0043 ,  ERC  (erc. europa. eu)( IT-DC 281987  and  HEALTH 2011-261366 ) and  CIC IGR-Curie 1428  (www. curie. fr). S. R. was supported by  MESR -Ministry of Higher Education and Research of France ,  MESR  fellowship (http://www. enseignementsup-recherche. gouv. fr/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Cell Viability Assay DLBCL cells were cultured in 96-well flat-bottom microtiter plates in the appropriate medium with FBS in the presence of increasing concentrations of mafosfamide (Santa-Cruz Biotechnology, Dallas, TX, USA), doxorubicin or etoposide (Selleckchem, Houston, TX, USA), or the BCL6 inhibitor 79-6 (Calbiochem, San Diego, CA, USA) for 4 days. The number of viable cells was determined using the Cell Titer Glo Luminescent Cell Viability Assay from Promega (Promega Corp, Madison, WI, USA). This test is based on the quantification of cellular ATP, as a marker of metabolically active cells, using a Centro LB 960 luminometer (Berthold Technologies, Bad Wildbad, Germany). Data were expressed as the mean percentage of six replicates and were normalized to the untreated control.
Gene Expression Profiling and Statistical Analyses Gene expression microarray data were from four independent cohorts of patients with DLBCL. The first cohort (i. e. , training cohort) included 233 patients treated with R-CHOP (R-CHOP Lenz cohort). Results were validated using the CHOP Lenz cohort (n = 181 patients treated with CHOP)  [14] , the Melnick cohort (n = 69 patients)  [15] , and the FFPE R-CHOP cohort (n = 72 patients)  [7]. The patients' pre-treatment clinical characteristics were previously described in References  [7, 14, 15]. Affymetrix gene expression data are publicly available via the online Gene Expression Omnibus (http://www. ncbi. nlm. nih. gov/geo/) under the accession numbers GSE10846 (Lenz cohorts), GSE23501 (Melnick cohort) and GSE53786 (FFPE R-CHOP cohort) (Affymetrix HG-U133 plus 2.
0 microarrays). Data were normalized with a Microarray Suite version 5. 0 (MAS 5. 0) using Affymetrix default analysis settings, and global scaling as the normalization method. The trimmed mean target intensity of each array was arbitrarily set to 500. Expression profiling data (accession number GSE56315) of DLBCL samples (n = 73) and normal centrocyte and centro-blast samples (n = 5/each) from human tonsils  [16]  were also compared. One or several probe sets were available for 24 of the 27 B-cell markers evaluated by MFC. In the presence of several probe sets for the same gene, the probe set with the highest coefficient of variation was retained (Supplementary Table  S1 ). Overall survival (OS) differences between groups were calculated using the log-rank test. Multivariate analysis was performed using the Cox proportional hazards model and Genomicscape (http://genomicscape.
CD39 is A Useful Marker to Discriminate Between ABC and GCB DLBCL Tumor Samples We then performed a significance analysis of microarrays (SAM) analysis of the probe sets that correspond to the 27 markers that we routinely use for the characterization of pathological mature B cells by MFC to compare their expression in ABC and GCB DLBCL samples with the aim to find additional markers to categorize tumor samples. For this analysis, we used the gene expression profiling data of ABC and GCB DLBCL samples (n = 167 and n = 183, respectively) from the CHOP and R-CHOP Lenz cohorts (non-classified tumor samples were excluded). Four of these 27 genes were differentially expressed in these two groups. CD10 and BCL6 were upregulated in GCB samples, whereas CD39 and BCL2 were upregulated in ABC samples (Figure  2A ).
Group 1 and group 2 did not reach the median OS, whereas group 3 and group 4 had a median OS of 46 months and 9 months, respectively (p = 3. 2E-11, Figure  4A ). The prognostic value of the score was validated in the CHOP Lenz cohort (n = 181) and in the FFPE R-CHOP cohort (n = 72), and showed a trend (p = 0. 06) in the Melnick cohort (n = 69) (Figure  4B-D ). We then asked whether the four genes included in the prognostic risk score (BCL2, BCL6, LAIR1, and CD11c) were differentially expressed between malignant and normal B cells. To this aim, we compared their expression in normal centrocytes (n = 5), normal centroblasts (n = 5), and DLBCL samples (n = 73) (GSE12195 dataset)  [26]. BCL2, LAIR1, and CD11c were significantly overexpressed in DLBCL samples compared with normal centrocytes (p = 0.
Methods plasmids. DNA sequences encoding the wild-type (WT) or mutant forms of the envelope or core proteins were inserted into pcDNA3. 1(+) (Invitrogen, Invitrogen Corporation Pontoise, France). PCR amplification was performed with the pRVHBV1. 5, a plasmid containing 1. 5 copy of a genotype A2 HBV genome, as the template (kindly provided by Dr Volker Bruss, accession number: X02763), and specific primers carrying BamHI and XhoI or AflII and ApaI restriction sites. A 6xHis-tag (His-tag) was also inserted in phase at the C-terminal end of the L and S protein sequences. Finally, L-His-Δ1, L-His-Δ2 and L-His-Δ3 mutants carrying deletions in preS1-preS2 domains were generated by overlap amplification with specific dedicated primers, using the L sequence as a template (Fig. 1 ). Seven core protein mutants carrying unique amino-acid substitutions were designed: core-S26A, core-T67A, core-L60A, core-L95A, core-K96A, core-I126A and core-Y132A (Q5 ® Site-Directed Mutagenesis Kit, NEB).
Acknowledgements This work was supported by the  Agence Nationale de Recherche sur le SIDA et les hépatites virales (ANRS)  ( ECTZ21256 ). FP was supported by a fellowship from the  Ministry of Research  and the  ANRS. We thank  V. Bruss  for provided us pRVHBV1. 5 and pRVHBV1. 5Δenvplasmids. We also thank people from the  Electron Microscopy platform (IBiSA) of Tours University  (http://microscopies. med. univ-tours. fr)for technical support.
CbsR12 binds to C. burnetii recombinant CsrA-2, but not CsrA-1, in vitro The in silico-predicted secondary structure of CbsR12 also revealed conserved single-strand sequence motifs among the various stem-loop structures (S1A Fig). This motif, AGGA/ANGGA, corresponds exactly to the conserved CsrA-binding motif of many bacteria  [34, 36, 38]. C. burnetii contains two annotated and distinct types of CsrA (termed CsrA-1 and CsrA-2) that share 65% primary sequence identity (data not shown). To test the functionality of these domains, we employed an in vitro binding assay and a RNA-protein electrophoretic mobility shift assay (EMSA) to determine if CbsR12 binds to natively-purified recombinant CsrA-1 (rCsrA1) and/or CsrA-2 (rCsrA-2). EMSA results clearly showed that CbsR12 binds to rCsrA-2, but not rCsrA-1, in vitro (Fig  1 ).
For example, in THP-1 cells, we saw a correlation between CbsR12 expression and growth rate from 1dpi to 3dpi. Furthermore, we found that maintenance of CbsR12 expression as the C. burnetii Tn327-Comp infection proceeded led to significantly larger CCVs at 7dpi. The apparent dysregulation of CbsR12 in the Tn327-Comp strain during infection of THP-1 cells is possibly due to the genomic context of the Tn327-Comp transposon-mediated insertion of cbsR12. We confirmed that the cbsR12 cassette inserted into an intergenic region between CBU_1788 and CBU_1789 (RSA493: accession number NC_002971. 4), and copy number qPCR analysis showed that the cassette only inserted once (data not shown). Although this cbsR12 cassette contained the cbsR12 gene and ~100-bp flanking regions, it is possible that a regulator exists >100bp upstream of the cbsR12 gene, and that this regulatory element is missing in the Tn327-Comp strain.
In silico and bioinformatics analyses RNA target predictions were carried out using TargetRNA2  [78] , IntaRNA  [79] , and CopraRNA  [80]  algorithms and default settings. RNA was folded using mFold  [81]  and visualized with the Visualization Applet for RNA  [82]. Analyses of RNA-Seq data were carried out as previously described  [31]. Briefly, raw fastq files were concatenated, quality-filtered with the FASTX toolkit (http://hannonlab. cshl. edu/fastx_toolkit/) and then clipped, aligned, and filtered with Nesoni version 0. 128 tools (http://www. vicbioinformatics. com/software. nesoni. shtml). Transcripts per million (TPM) were calculated using custom perl and python scripts that can be accessed through GitHub (https://github. com/shawachter/TPM_Scripts). Crosslink-Seq enrichment was accomplished by processing.
bam files using featureCounts  [83] , followed by use of the DESeq2 package in R version 3. 4. 4 to obtain differentially expressed genes  [84]. The Artemis genome browser was used to visualize generated alignment files (http://www. sanger. ac. uk/science/tools/artemis)  [85]. All IFA images were processed and analyzed using Fiji  [86]  and Cell Profiler  [87] , respectively. Figures were created using R version 3. 4. 4, Inkscape (https://inkscape. org/release/inkscape-0. 92. 4/) and GIMP (https://www. gimp. org/downloads/).
The total RNA mixture hybridized to the biotinylated anti-CbsR12 RNAs were mixed with the resin and incubated for 4 hours at 4°C. The resin was then washed 5 times in WB400 buffer (20 mM HEPES pH 8, 10 mM MgCl2, 400 mM KCl, 0. 01% NP40, 1 mM DTT) and the hybridized RNAs were re-isolated with TRI Reagent. The remaining RNAs were un-crosslinked by exposure to short-wave UV light (254 nm) for 15 minutes on ice. The resulting RNA was sent to the Yale Center for Genomic Analysis for RNA-Seq. The sequencing reads are available at the NCBI sequencing read archive (accession number: SUB5191080).
Furthermore, although IL-21 has previously been shown to be produced by CXCR3 + TH1 cells, this could not be detected in our experimental system in either control or Tbx21ΔT mice (data not shown)  (Miyauchi et al. , 2016). We next sought to determine the overall affect that T cell-intrinsic loss of T-bet had on GC B cell responses. We examined GC B cell differentiation by flow cytometry at d8 following influenza infection in Tbx21ΔT and CD4Cre mice. We detected an increase in the frequency and total number of GC B cells within dLN, in line with the increased expansion of TFH cells in Tbx21ΔT animals (Figures  6D  and 6E ). However, we did not see any overt effect on the proportion of cells in the LZ (CXCR4 -CD86 + ) or DZ (CXCR4 + CD86 -) compartments (Figure  6F ; Figure  S5A ). Furthermore, we quantified the number and size of GC structures (GL7 + IgD -) in dLNs.
, 2016). Cytometry data was acquired using a LSR Fortessa x-20 (BD Biosciences). Immunofluorescence staining-Lymph nodes were fixed in 4% paraformaldehyde and immersed in 30% sucrose before being embedded in OCT compound (Tissue-Tek). Tissues were cut via microtome (Leica) into 12-20 μm sections and mounted on Superfrost Plus slides. Sections were stained as described previously  (Rankin et al. , 2013)  using described antibodies (Key Resource Table ). Images were acquired using a LSM780 confocal microscope (Carl Zeiss MicroImaging). The acquisition software was Zen Black 2012 and images were quantified with ImageJ (NIH). Enzyme-linked immunosorbent assay (ELISA)-Antibodies for detection of total serum are listed (Key Resource Table ). 96-well flat-bottom ELISA plates (Sarstedt) were coated with 75 μL per well of respective unlabeled coating antibody (diluted in PBS) 4°C overnight.
Plates were blocked with PBS 1% BSA at room temperature for 1h. Plates were washed three times with PBS Tween20 and dH2O. Samples and standards were added to the wells and blank wells were included (PBS 1%BSA). Plates were incubated for 2-4h at 37°C before being washed three times. Secondary antibody, HRP (horseradish peroxidase) diluted in PBS 1%BSA was added and incubated at 37°C for 2h. The plates were then washed five times and 100 μL of OPD (o-phenylenediamine dihydrochloride) substrate solution (ThermoFisher Scientific) was added and plates incubated room temperature in the dark for 10-30 minutes to develop. OD values were read at 405nm using a plate reader (BMG Labtech) and the data was evaluated using MARS (BMG Labtech).
QUANTIFICATION AND STATISTICAL ANALYSIS Flow cytometry was analyzed using Flowjo (Treestar) and statistical significance was determined using the paired (for chimeric experiments) or unpaired (two-tailed) Student's t test. For ELISA, differences between the groups were analyzed using the MannWhitney non-parametric two-tailed test with a 95% confidence. All experimental data is presented as mean ± standard error of the mean (SEM) with statistical analysis performed using Prism 7 (GraphPad Software).
Supplementary Material Refer to Web version on PubMed Central for supplementary material. (G-J) Plots (G and I) and frequency and total number (H and J) of TH1 (NP 311 + CD44 + PSGL-1 + Ly6c + ) (G and H) and TFH (NP 311 + PSGL-1 -Ly6c -PD-1 + CXCR5 + ) (I and J). Data are representative of three independent experiments, n = 3-5 mice/group. Data are mean ± SEM. (E-J) CD4cre and Tbx21ΔT mice were infected with LCMV, and splenic CD4 + CD44 + antigen-specific cells were analyzed at d8 p. i. Plots (E) and frequency and total number (F) of GP 66 CD4 + T cells. Plots (G and I) and frequency and number (H and J) of TH1 (GP 66 + CD44 + PSGL-1 + Ly6c + ) (G and H) and TFH (GP 66 + CD44 + PSGL-1 -Ly6c -PD-1 + CXCR5 + ) (I and J). Data are representative of three independent experiments, n = 3-5 mice/group.
ACKNOWLEDGMENTS We thank members of the  Groom lab  for technical assistance and  Leisa-Rebecca Watson , who performed initial experiments. This work was supported by  National Health and Medical Research Council (NHMRC)  project grants to J. R. G. and  K. L. G. -J. ( GNT1137989  and  GNT1057707 ) and to M. P. ( GNT1006592 ,  GNT1045549 , and  GNT1065626 ) and by  NIH  grants to  J. J. M. ( R01 AI107020  and  R21 AI124143 ). A. A. S. and B. C. D. are supported by a  University of Melbourne  research scholarship. J. R. G. is supported by an  Australian Research Council   Future Fellowship  ( FT130100708 ). K. L. G. -J. is supported by an  NHMRC   Career Development Fellowship  ( 1108066 ). G. T. B. and S. L. N. are supported by an  NHMRC   Senior Principal Research Fellowship. K. L. G. -J. and M.
Patients Patients addressed for complement testing often present, amongst others, recurrent microbial infections, unexplained inflammation, auto immune disease (AID), or renal diseases. Patients included in this study came from different French departments of medicine. Complement deficient patients (273 index cases and 48 asymptomatic related individuals) diagnosed between 1988 and 2018 were reported in this study including 75 previously published patients  (9, 15, 20, (24) (25) (26). Clinical data were available for 84% of the cohort (n = 230) (Table  1 ). Genetic screening was possible for 212 patients.
DISCUSSION We here report the spectrum of complement genes pathogenic variants in the largest worldwide cohort of patients with subtotal or total complement deficiency. Except for FP, we posited autosomal recessive transmission of the abnormal allele in these families and sought homozygous or compound heterozygous variants within the same gene. Variant frequency in the general population is a key criterion used in the clinical interpretation of sequence variants. As strong diseasepredisposing alleles are likely to be deleterious, we focused on rare variants with a minor allele frequency below 0. 1%. In our study a total of 48 variants (48/107, 45%) were not found in the Genome Aggregation Database (gnomAD) that has the ability to identified ultra-rare variant. However, 9 variants (9/48, 19%) not detected in gnomAD, have been already reported in patients with CD from unrelated families, including 5 variants (5/10, 50%) identified in FP deficient patients suggesting a common ancestral origin for these groups of families or a mutational hotspot  (36) (37) (38).
ACKNOWLEDGMENTS We wish to acknowledge physicians who participated to the study:  Lubka Roumenina ,  Marie-Agnès Dragon-Durey ,  Lionel Galicier ,  Brigitte Bader Meunier ,  Nizar Mahlaoui ,  Despina Moshous , and  Felipe Suarez. The authors thank  J. Blouin  and  N. Poulain  for their excellent technical assistance.
FUNDING This work was supported by grants from the  Délégation Régionale à la Recherche Clinique, Assistance Publique -Hôpitaux de Paris  ( Programme Hospitalier de Recherche Clinique  ( AOM08198 ), by the  EU   FP7  Grant  2012-305608  (EURenOmics), the  Fondation Du Rein  (FRM, Prix 2012 FDR), the  Association pour l'Information et la Recherche dans les maladies Rénales génétiques (AIRG France ).
Conversely, other retrospective studies did not confirm the predictive value of NLR across different randomized chemotherapy arms in colorectal cancer  (18) , or in advanced biliary tract carcinomas, in which low NLR values during chemotherapy were not associated with significant improvement in survival (only high NLR baseline values that decreased under chemotherapy predicted a significant better mOS)  (19). NLR pretreatment values were not predictive of outcome in prostate cancer patients treated with docetaxel  (20). Variation in NLR values during targeted therapy seems to be an interesting biomarker of response in metastatic renal cell carcinoma patients  (19). Templeton et al. reported a retrospective analysis of 1,199 patients treated with targeted therapies (bevacizumab, axitinib, sorafenib, sunitinib, temsirolimus) from the metastatic renal cell carcinoma database consortium and highlighted the predictive value of NLR variation between baseline (before targeted therapy commenced) and at 6 weeks (±2 weeks).
Identification and Quantification of TANs in Cancer Patients The clinical relevance of evaluating pro-and anti-tumor functions of TANs is highly pertinent in cancer patients, since TAN infiltration was reported to predict either poor  (62) (63) (64) (65) (66)  or good prognosis  (67) (68) (69). Conditions that differ between good and poor prognostic TANs will be discussed below. Although the methods of analysis of survival across studies were similar, identification of quantification methods of TANs infiltrating human tumors varied greatly. Hematoxylin & Eosin (H&E) staining remains a good approach to quantify TAN infiltration based on the unique segmented-nucleus morphology of neutrophils. Scanned-tumor slides stained with H&E are accessible from TCGA public database and have already been used for TAN quantification  (70).
However, this approach may under-estimate the potential infiltration of immature neutrophils since the banded-nucleus morphology of immature neutrophils is less distinguishable from other immune cells. TAN infiltration can be also quantified through immunostaining, using antibodies against markers of neutrophils, such as CD66b and CD15. Neutrophils and eosinophils share numerous markers as they are closely related ontologically speaking, and very few studies took this cofounding effect into account. Evidence showed that CD66b is expressed at the same level between eosinophils and neutrophils in peripheral blood of patients with rheumatoid arthritis  (71). For CD15 (Sialyl-Lewis X), although expressed on both eosinophils and neutrophils, data showed that it is 10-100 times higher on neutrophils than eosinophils  (72).
Collectively these results demonstrate that disrupting the physiological balance of L-arginine may either inhibit or promote tumor progression depending on T-cell and tumor cell susceptibility to arginine starvation. The synergistic therapeutic efficacy of either recombinant arginase 1 or arginase 1 inhibitor in combination with various chemotherapies or Pembrolizumab (anti-PD-1) is currently being tested in phase 1/2 clinical trials (NCT03371979; NCT02903914; NCT03361228; NCT03314935).