dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank  Dr. Stephane Manenti  for discussion and critical reading of the manuscript ;  Dr. Hellen Robey  and  Dr. Pauline Gonnord  for discussion. We thank  Dr. Liza Filali  for advice in image analysis and  Dr. Juliet Foote  for critical reading of the manuscript. We thank the flow cytometry and imaging core facilities of the  INSERM UMR 1043, CPTP  and of the INSERM UMR 1037, CRCT, Toulouse, France. This work was supported by grants from  the Laboratoire d'Excellence Toulouse Cancer (TOUCAN)  (contract  ANR11-LABEX ),  Region Occitanie  (contracts RCLE  R14007BB ,  671 34 No 12052802 , and  RBIO R15070BB , No  14054342 ),  Fondation Toulouse Cancer Santé  (contract  2014CS044 ) from the  Ligue Nationale contre le Cancer  (Equipe labellisée 2018) and from  Bristol-Myers Squibb  (No  CA184-575 ).
Genotyping of E. bieneusi Isolates by ITS Sequencing Genotyping of E. bieneusi isolates was performed by nested-PCR amplification of the entire ITS region (243 bp)  [20]  using the outer primers EBITS3 and EBITS4 (Table  S2 ) and the inner primers EBITS1 and EBITS2. 4 (Table  S2 ). These reactions produced frag-ments of 435 and 390 bp, respectively. The ITS genotypes of E. bieneusi were named according to the established nomenclature  [19]. All PCR products were purified and sequenced in both directions according to the Sanger method using an ABI PRISM 377 DNA Sequencer (Applied Biosystems, Foster City, CA, USA). The resulting sequences were analyzed using BioEdit Sequence Alignment Editor Version 7. 0. 5. 3 and compared with equivalent sequences available at the National Center for Biotechnology Information (NCBI) database using the Blastn suite of algorithms (https://blast.
ncbi. nlm. nih. gov/Blast. cgi (accessed on 24 February 2021)). To correct for wrongly assigned E. bieneusi genotypes on NCBI, we additionally relied on comparison with sequences provided by Li et al. (2019, Tables  S2-S12 )  [13] , where authors updated the associations between E. bieneusi ITS genotypes and NCBI accession numbers/entries available to date. Nucleotide sequences obtained in this study were deposited at the GenBank database under accession numbers MN596813-MN596826 and MW272504-MW272506.
Phylogenetic Analyses The ITS sequences of the present study were compared to an equivalent set of sequences from previously characterized E. bieneusi genotypes identified in humans, animals, and the environment. A phylogenetic analysis was performed through the construction of a neighbor-joining tree using Mega X program version 10. 0. 5 (http://www. megasoftware. net/ (accessed on 24 February 2021)), Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, USA) based on the evolutionary distances calculated by the Kimura-2-parameter model. A bootstrap analysis was used to assess the robustness of the resulting clusters using 1000 replicates. Population structure analyses (i. e. , minimum-spanning tree) were performed using Bionumerics version 7. 6 (Applied Maths, Sint-Martens-Latem, Belgium).
As expected, genotypes WL12 (n = 10), Peru8/WL12 (n = 2), and WL12-like (n = 1) clustered together, infecting in total 13 patients out of 17 (blue spheres, Figure  2 ). Genotypes D and Type III formed singletons (pink and green spheres, Figure  2 ), whereas genotypes HNM-III and A/B/S5 were more distantly related to WL12, forming a cluster that differs by at least three substitutions (brown and purple spheres, Figure  2 ). Our minimum-spanning tree analysis revealed that the 17 E. bieneusi infection cases were associated with four distinct genotypic clusters and confirms the high prevalence of genotype WL12 in our patient population. a Each patient was assigned an identification number according to the chronology of sampling (see Table  S1  for a complete description); b NCBI accession number for each E.
bieneusi internal transcribed spacer (ITS) sequence identified in this study. c Underlying disease for each E. bieneusi-positive patient according to the information provided in Table  S1. d ITS genotype was assigned following a BLAST analysis of each ITS sequence against those available at the NCBI database. e The NCBI accession numbers of identical or similar genotypes (indicated in bold) are provided. For similar genotypes, only one single nucleotide polymorphism (shown between parentheses) differentiates the genotypes identified in this study from the known ones. The size of the spheres is proportional to the number of strains sharing the same genotype. The shaded zones (light gray) indicate clusters of similar ITS genotypes (i. e. , ITS sequences with no more than one nucleotide difference).
Note: All commercial confocal microscopes are provided with software packages that enable merging and 3D reconstructions. Alternatives: ImageJ/FIJI (NIH) can also be employed for merging and 3D reconstruction, respectively. 12. Widefield microscopy imaging a. Adjust illumination power and exposure time of each channel to satisfy the dynamic range of the experiment by imaging 2-3 control and 2-3 treated samples b. Once illumination power and exposure time are selected, image at least 3 fields per coverslip on individual blue (DAPI), red (cytosolic dsDNA) and green (LMNB1, COX4 or TFAM) channels (see above for excitation/emission peaks and laserline/detector settings) c. Single-channel images can be merged for visualization purposes and/or subjected to automated analysis for quantitative assessments, as detailed below CRITICAL: Illumination power and exposure time should be adjusted to prevent overexposure of each fluorophore even in the brightest condition.
Automated image analysis Timing: 1 day Single-channel widefield microscopy images are processed for the assessment of number of cytosolic dsDNA-positive foci in each cell, percentage of cells containing cytosolic dsDNA-positive foci (Figure  1 ), and spatial localization of cytosolic dsDNA-positive foci with respect to COX4, TFAM or LMNB1. Note: Photoshop (Adobe), ImageJ/FIJI or equivalent imaging software is employed to adjust images prior to automated analysis.
, 2011). Note: Before running the actual analysis, it is recommended to test each step using the ''Test Mode''. 14. Identification of nuclear and cytosolic objects with Cell Profiler a. Identification of nuclear objects i. Identify nuclear objects as objects of 40-110 pixels diameter in blue-channel images with the ''IdentifyPrimaryObjects'' package Alternatives: In case of LMNB1 co-staining, green-channel images can be equally employed to define nuclear objects as above. ii. Discard objects outside this diameter range as well as objects on the image borders iii. Identify clumped objects by ''Shape'' and separate them based on ''Signal intensity''. Turn on the following additional features ''Automatically calculate size of the smoothing filter for declumping'' and ''Automatically calculate minimal allowed distance between local maxima''.
b. Identification of cytoplasmic areas i. On blue-channel images with identified nuclear objects that are used as input objects, identify cellular objects with the ''IdentifySecondaryObjects'' package, by the distance -N method expanding the primary object by 50 pixels ii. Using the package ''IdentifyTertiaryObjects'', define cytoplasmic areas subtractively based on cellular and nuclear objects Note: The optimal extent and method of expansion to identify cellular objects may vary considerably with cell type and magnification. Visual comparison of cytoplasmic areas in processed images vs. cells in enhanced green-channel images can be used to assess fidelity and alter the extent of expansion if needed. Alternatives: In case of LMNB1 co-staining, green-channel images can be equally employed to define cytoplasmic areas as above.
Note: The optimal diameter for this step may vary considerably with cell type and magnification. CRITICAL: Insufficient or excessive enhancement largely compromises automated image analysis as it prevents proper identification of prevents correct identification of primary objects and hence precise quantitative assessments (see troubleshooting 5). b. On enhanced images, identify dsDNA + , COX4 + or TFAM + objects as objects of 4-25 pixels diameter on red-or green-channel images, depending on the secondary antibodies employed for IF, with the ''IdentifyPrimaryObjects'' package c. Discard objects outside this diameter range as well as objects on the image borders d. Identify clumped objects by ''Shape'' and separate them based on ''Signal intensity''. Turn on the following additional features ''Automatically calculate size of the smoothing filter for declumping'' and ''Automatically calculate minimal allowed distance between local maxima'' e.
Note: The optical resolution of our system ($0. 32 mm) is intrinsically insufficient to resolve cytosolic dsDNA + foci closer to each other of <0. 32 mm, implying that quantitative results will suffer from some degree of underestimation, especially in presence of cytosolic dsDNA + foci at high density. 16. Identification of cytosolic dsDNA + objects with Cell Profiler a. Relate dsDNA + objects and cytoplasmic areas (defined as above) as child and parent, respectively, with the ''RelateObjects'' package b. Using the ''FilterObjects'' package, select cytoplasmic areas containing a minimum value of 1 children object as positive cells. Note: Thresholds should be validated for each set of images as they might vary in different cell types, across experiments and/or with different image acquisition devices.
''Test mode'' should be used to validate that the parameters are adequate for each set of images. Specifically, the value of children objects employed to define positive cells is selected as the closest integer number below to the average number of cytosolic dsDNA + dots observed in untreated cells (in our settings, 1. 46 see Table  1 ) Optional: Quantitative results and images exemplifying the analytic pathway can be exported in multiple file formats c. Interrogate quantitative data for number of cytosolic dsDNA + dots per cell and percentage of cells exhibiting cytosolic dsDNA + dots. 17. Colocalization with Cell Profiler a. Nuclear dsDNA + objects i. Relate dsDNA + objects and nuclear objects (defined as above) as parent and child, respectively, with the ''RelateObjects'' package ii. Using the ''FilterObjects'' package, identify filtered dsDNA objects as nuclear by children category and nuclear count with a minimum value of 1 Optional: Quantitative results and images exemplifying the analytic pathway can be exported in multiple file formats iii.
Interrogate quantitative data for percentage of dsDNA + objects co-localizing with nuclear (DAPI + or LMNB1 + ) objects over total dsDNA + objects b. dsDNA:COX4 or dsDNA:TFAM co-localization i. Relate dsDNA + objects and COX4 + or TFAM + objects (defined as above) as child and parent, respectively, with the ''RelateObjects'' package ii. Using the ''FilterObjects'' package, filter results using 1 as a minimum measurement value (COX4 + or TFAM + objects that do not co-localize with dsDNA + objects are excluded from further analysis) Optional: Quantitative results and images exemplifying the analytic pathway can be exported in multiple file formats iii. Interrogate quantitative data for percentage of dsDNA + objects co-localizing with COX4 + or TFAM + objects over total dsDNA + objects Pause point: Automated analysis can be paused indefinitely at any of the steps above.
QUANTIFICATION AND STATISTICAL ANALYSIS Quantitative results obtained upon automated image analysis as detailed above are processed as follows. Cytosolic dsDNA data are quantified as mean dsDNA + dots per positive cell or percentage of positive cells (containing at least 1 dsDNA + dot) in each image. Co-localization is quantified as percentage of dsDNA + foci with overlapping COX4, TFAM or LMNB1 signal in each image. A minimum of 3 fields from a total of at least 2 biologically independent samples collected over a minimum of 2 independent experiments (6 data points, n = 2) ensures statistical validity. Especially for particularly heterogeneous cell populations, however, we recommend to analyze at least 3 fields from a total of at least 3 biologically independent samples collected over a minimum of 2 independent experiments (9 data points, n = 3).
ACKNOWLEDGMENTS We are indebted to Jeffrey Kraynak from the WCM Radiation Biology Core Facility (established with support from an NIH/NCI Small Instrumentation Grant to Dr. Formenti, #S10-RR027619-01) for help with irradiation procedures. The LG lab is supported by a Breakthrough Level 2 grant from the US Department of Defense (DoD), Breast Cancer Research Program (BRCP) (#BC180476P1), by the 2019 Laura Ziskin Prize in Translational Research (#ZP-6177, PI: Formenti) from the Stand Up to Cancer (SU2C), by a Mantle Cell Lymphoma Research Initiative (MCL-RI, PI: Chen-Kiang) grant from the.
Overcoming Bypass Resistance Bypass tracks warrant a dual blockade of the founder mutation and the acquired resistance alteration. This strategy has proved to be safe and effective in the phase I TATTON trial evaluating savolitinib, a MET inhibitor, in combination with osimertinib in EGFR-mutated NSCLC  (118). In individual case reports, acquired fusions have also benefited from a dual blockade  (refs. 56, 61, 63, 71, 73, 119-124 ; Supplementary Table  S2 ). Also, many drug combinations have proved to reverse resistance in in vitro studies, but clinical validation is pending (Supplementary Table  S2 ). The ongoing phase II ORCHARD study (NCT03944772) evaluates combination strategies for bypass tracks in patients with NSCLC failing first-line osimertinib. Bispecific antibodies are also promising treatment strategies.
In preclinical studies on wild-type and mutant EGFR NSCLC with c-MET pathway activation, the bispecific antibody targeting EGFR and c-MET (JNJ-61186372) demonstrated antitumor activity through target inhibition, enhanced antibody-dependent cell-mediated cytotoxicity, and FcγRIIIa binding  (125). A phase I clinical trial is currently ongoing (NCT02609776). The prevention of acquired resistance is another valuable strategy. In BRAF V600E -mutated melanoma or NSCLC, MEKi are combined with BRAFi to prevent MAPK pathway reactivation, which frequently occurs under single-agent BRAFi  (64, 126). Interestingly, aside from increasing efficacy and preventing resistance, the combination of MEKi and BRAFi also decreases cutaneous toxicities observed with paradoxical MAPK activation under BRAFi monotherapy  (127, 128).
g. , ADVISE, NCT03335540; PIONeeR, NCT03833440). The preliminary results of the PIONeeR trial suggest a predictive value for PD-L1 density on all cell types, PD-L1 tumor expression, infiltrating cytotoxic T cells, and immunosuppressive cell density. Of note, nonresponding patients with high PD-L1 tumors actually had low PD-L1 density on all cell types (tumor and stromal; ref. 293).
Thus, Helios may directly regulate genes involved in lineage specification, but not stemness, in HSPCs. We then evaluated the effect of Helios binding on chromatin accessibility, by integrating the HPC7 ChIP-seq data with an analysis of accessible chromatin between WT and Helios KO LSK cells by assay for transposase-accessible chromatin using sequencing (ATAC-seq). This showed that Helios does not modify the location or relative number of accessible regions, as the >130 × 10 3 regions were mostly shared between WT and KO DNA. In all, 10,890 regions bound by Helios ("Helios sites") overlapped with ATAC-seq peaks. Helios binding markedly coincided with chromatin closing in WT cells, with a clear bias toward increased accessibility in the KO chromatin (Fig. 7  A, all ATAC peaks). We evaluated the importance of Helios sites at regions where the ATAC-seq peaks were strongly (log2 fold change [log2FC] >0.
The HSPCs identified by their PC1 and PC2 coordinates were therefore separated according to their proliferative status along the vertical axis of the UMAP plot; quiescent cells (blue) were found at the top, while cells at early (orange) and late (green) stages of the cell cycle were clustered at the bottom (Fig. 8 A ). PC3 genes were associated with lineage priming (Fig. 8  B; and Fig. S5, B  and E ). The cells identified by negative PC3 values expressed high mRNA levels for genes associated with HSCs and MPP2 cells (e. g. , Mpl, Gata2, and Cd9) according to our RNA-seq data and the ImmGen datasets (MPP2). These cells clustered to the left of the UMAP plot (Figs. 8 B and S5 E). In contrast, the cells identified by positive PC3 values expressed abundant transcripts for genes characteristic of lymphoid-primed MPP4 cells (e.
g. , Flt3, Dntt, and Cd53), and they clustered to the right. The cells in the middle expressed PC3 genes at intermediate levels compared with HSC/MPP2 and MPP4 cells and probably corresponded to MPP3 cells (Fig. S5 E ), suggesting that myeloidbiased MPPs express genes associated with both megakaryocytes and lymphocytes. Similar results were obtained when we compared the cells expressing CD150 (encoded by the Slamf1 gene) and CD48 at the mRNA and protein levels (Fig. S5 F ): CD150 + CD48 -cells were located to the left (as were Slamf1 + Cd48 - and Cd34 -Cd48 -cells), and CD150 -CD48 + (and Slamf1 -Cd48 + ) cells (which included MPP3 and MPP4 cells) were mapped to the center and the right. Approximately 500 cells in the dataset corresponded to HSC/MPP2 cells, as defined by their Mpl + Flt3 -(n = 468; Fig.
Flow cytometry and cell sorting Cells were stained with the Ab combinations listed in Table  S2  in PBS/0. 5% BSA/2 mM EDTA. Samples were acquired on a LSR II or Fortessa analyzer (BD Biosciences) and analyzed with FlowJo10 software. For intracellular staining, cells were fixed overnight (ON) at 4°C and then permeabilized and stained with the primary Ab for 1 h at room temperature and, when necessary, with a secondary Ab for 1 h on ice using the eBioscience Foxp3/Transcription Factor Staining Kit (Thermofisher). For cytokine staining, CD4 + T cells were first magnetically enriched using the Dynabeads Untouched Mouse CD4 Cells Kit (Thermofisher) and then stimulated for 2 h at 37°C with PMA and ionomycin (0. 5 μg/ml each) in the presence of BD GolgiPlug (BD Biosciences) in IMDM supplemented with 10% FCS, MEM, Hepes (10 mM), GlutaMAX (Gibco), sodium pyruvate (1 mM), and 2-mercaptoethanol (50 μM).
6. 1p1), with annotations from ENSEMBL GRCm38 and "union" mode. Differential gene expression analyses were performed using the Bioconductor package DESeq2 (v1. 16. 1) on R (v3. 3. 2). FC rank ordering statistics was used to compute f-values  (Dembélé and Kastner, 2014) , which correspond to a method based on ranks of the FCs of all pairwise comparisons. Differentially expressed genes were selected according to the following criteria: (1) adj P < 0. 20, (2) f-value <0. 02 or >0. 98, and (3) normalized reads per kilobase >20 for all samples in either the WT or KO group. Heatmaps of differentially expressed genes were created using Cluster and Java TreeView software. For the GSEA, the KO versus WT FCs of all genes detected in the RNA-seq experiments were used for the ranked genes lists. The MPP4 signature was obtained using our transcriptome data.
Gene expression and library analyses The 39 mRNA-seq library reads were processed using CellRanger pipeline v3. 0. 2 on mm10 and ENSEMBL GRCm38. Cell hashingand CITE-seq-derived library reads were merged together and processed with 39 mRNA-seq library using the Feature Barcoding Analysis option. Tag identification from the HTO library was performed using the approach proposed by the Seurat R package v3. 0. 0 and R v3. 5. 1 on HTO counts  (Butler et al. , 2018; Stoeckius et al. , 2018). Further, the CellRanger count was used to filter out low-quality cells (doublets and/or mitochondrial genes). To detect and remove doublets, the distribution of the total number of unique molecular identifier (UMI) of each cell was computed for the three libraries. The filtering threshold used corresponded to the 99th percentile of the distribution.
Hence, the threshold was 32,883 UMIs for the 39 mRNA library, 740 UMIs for the ADT library, and 3,234 UMIs for the HTO library. Further, we filtered out cells with >5% mitochondrial gene contamination (indicating possibly poor quality or dying cells). PCA of the datasets were performed with Seurat R package Satija Lab, v2. 2  (Satija et al. , 2015). The three first PCs, containing the highest SD, were further used to run a UMAP nonlinear dimensional reduction. UMAP coordinates were subsequently visualized with Loupe browser (10X Genomics) for further analysis. The Loupe browser was used to "label" and export the cell population of interest. The Loupe manual filtering option, using the PC1-PC2 genes or K-means clustering (K = 2 or 3), was used to identify proliferative and quiescent cells on the UMAP.
Data were analyzed using the ENCODE ATAC-seq pipeline v1. 5. 1. Reads were aligned to mm10 using Bowtie, and peak calling was performed using MACS2 v2. 2. 4. The peaks detected in each sample were combined using Bedtools merge v2. 26. 0. Reads were normalized across libraries  (Anders et al. , 2015). Comparisons of interest were performed using the method proposed by the DESeq2 Bioconductor library (DESeq2 v1. 24. 0). Visualization of normalized data were generated from post-processed files using Homer make UCSC file v4. 11. 1 with the "-norm 3e7 -style dnase" parameter  (Anders et al. , 2015). Identification of the ATAC-seq peaks bound by Helios was performed by intersecting the intervals of the Bedtools (v2. 29. 0; https://usegalaxy. eu) with ATAC-seq regions (KO vs. WT P < 0. 05) and with Helios ChIP-seq peaks using a pileup >15.
The numbers above each bar indicate the number of Helios-bound peaks (top) within the total number of peaks in the subset (bottom). Hypergeometric test: *, P < 0. 02; ***, P < 10 -10. (C) Volcano plots showing log2FC versus -log2(adj p) for Helios-bound MkP and MPP4 genes or a set of random genes. Red dots highlight regions for which -log2(adj p) was >3. 5. (D) Integrative genome viewer screenshots of representative platelet genes. Shown are ATAC-seq signals in WT and KO LSK cells and Helios, Gata2, and Runx1 binding in HPC7 cells (Gata2 and Runx1 data are from the GEO dataset GSE22178). Boxed regions belong to the regions selected in C as having significant increases of ATAC-seq signals in KO cells. (E) Seq-miner heatmap showing 4,370 regions with significantly increased ATAC-seq signals in KO versus WT LSK cells (adj P < 0.
Acknowledgments We thank members of the  Chan-Kastner laboratory for scientific discussion  and help and  P. Marchal  for technical support. We thank  Steve Emerson  for critical discussions. We thank  C. Keime  and  D. Dembélé  for help with the RNA-seq data analysis, the IGBMC GenomEast platform for help with microarrays and sequencing, the  IGBMC flow cytometry facility (C. Ebel) , the IGBMC animal facility ( M. Gendron ,  S. Falcone ,  W. Magnant , and  A. Vincent ), and the IGBMC cell culture facility. S. Chan  and  P. Kastner  received funding from the  Agence Nationale de la Recherche  ( ANR ;  ANR-11-BSV3-018-01  and  ANR-17-CE15-0023-01 ); the  Institut National du Cancer (INCa ;  PLBIO-2015-114 );  Ligue Nationale Contre le Cancer  (LNCC; Equipe Labellisée 2015-2017);  Fondation pour la Recherche Médicale  (FRM; Equipe FRM 2019, EQU201903007812);  Fondation ARC ;  LNCC Grand Est/Bourgogne Franche Comté ; institute funds from  INSERM ,  CNRS ,  Université de Strasbourg , and the institute grant  ANR-10-LABX-0030-INRT , a  French State  fund managed by the  ANR  under the  frame program Investissements d'Avenir   ANR-10-IDEX-0002-02 ; and an  LNCC Grand Est/Bourgogne Franche Comté equipment grant  ( 001K.