dataesr/scientific-paragraphs-categorization huggingface dataset:
Data are from 2 independent experiments where 10 NE rupture events per experiment were analyzed. (M) Cells stably expressing 53BP1-EGFP and icGAS were confined at 2 µm, harvested and plated for 20 minutes before fixation for immunostaining with antibodies against GFP (left) or γH2AX (right); box and whisker plot show the median value and 10-90 percentiles. P values were calculated by unpaired Student's t-test, except for items "H" and "I" (One-way ANOVA with post hoc Tukey test). ***P < 0. 0001; **P < 0. 005; ns = not significant. See also Fig. S2  and Movies S2, S3 Figure  3. DNA damage upon NE rupture is TREX1-dependent. (A, B) A variety of TREX1deficient or -proficient cells was confined at 2 µm and DNA damage level was assessed after 2 h under confinement. Graphs represent the mean ± SD of 3 independent experiments where 20 cells were analyzed per condition per experiment.
Staining was revealed using Novolink Polymer Detection Systems (Novocastra) and SuperSensitive Link-Label IHC Detection System Alkaline Phosphatase (Biogenex). DAB (3,3'diaminobenzidine) and Vulcan Fast Red were used as substrate chromogens. The slides were counterstained with Harris hematoxylin (Novocastra). All the sections were analyzed under a Zeiss AXIO Scope. A1 microscope (Zeiss, Germany) and microphotographs were collected using a Zeiss Axiocam 503 Color digital camera using the Zen2 imaging software.
Measurements of γH2AX intensity relative to tumor edge in mice xenografts For semi-automated analysis of nuclear γH2AX intensity in tissues as a function of distance from tumor edge, a macro was written and run on Fiji software. First, individual nuclear regions were obtained after thresholding using the DAPI channel. Subsequently, the tumor edge was manually defined on the image using the myoepithelial layer as the reference. Next, a map of the distance to the tumor edge was obtained. Finally, γH2AX intensity and the distance to the edge was measured for each nuclei of the tumor xenograft. Cells with strongly condensed chromatin (as assessed by DAPI staining) were excluded from the analysis as they could be either mitotic or apoptotic cells. Due to intensity variation between experiments, γH2AX intensities were normalized using Z scores.
QUANTIFICATION AND STATISTICAL ANALYSIS Statistical analyses for all experiments were performed in Prism (GraphPad) v5. 02. Statistical data are presented as median or mean ± SEM, SD or 10-90 percentiles. Sample size (n), statistical tests used and p-values are specified in the figure legends. Samples in most cases were defined as the number of cells counted/examined within multiple different fields of view on the same dish/slide, and thus represent data from a single sample within a single experiment that are representative of at least three additional independently conducted experiments. showing the 53BP1-EGFP total intensity (blue) and the average nuclear background intensity (red) of RPE1 cells while under confined at 2µm. Data are pooled from 3 independent experiments where a total of 50 cells per condition were analyzed.
Subsequently cells fixed and stained for endogenous TREX1. Bar, 25 µm. (H) Frequency distribution of the number of nuclear blebs in RPE1 TREX1 KO clones and in parental RPE1 cells. Data represents 3 independent experiments where 60 cells per experiment were analyzed. (I) RPE1 cells transiently transfected with TREX1-EGFP WT were confined at 2 µm using a pressurecontrolled dynamic confiner. Images represent a single plane Z slice through the middle section of the nucleus. Bars, 5 µm and 1 µm (magnification). Images are representative of 2 independent experiments. (J) Quantification of DNA damage levels (number of 53BP1 foci) in DCIS wt cells and DCIS TREX1 KO #1 cells stably expressing 53BP1-mCherry reconstituted with the indicated TREX1 constructs fused to GFP and confined at 2 µm. Quantification was performed in cells displaying NE rupture.
For other panels, P values were calculated by One-way ANOVA with post hoc Tukey test in "J". **P < 0. 005; *P < 0. 05; ns = not significant. See also Table  S2  and Movie   4 ). (A) RPE1, MCF10A and DCIS cells were treated with doxorubicin (40 nM), etoposide (25 µM) or DMSO (vehicle) for the indicated lengths of time. Cells were then fixed and processed for β-gal staining. The percentage of βgal-positive cells was plotted. Graph, data represents mean ± SD of 3 independent experiments where 200 cells were scored per condition per experiment. (B) Frequency distribution of the designated categories for cells under the indicated confinement heights. Data represents 3 independent experiments where 37 non-confined cells, 40 confined cells with non-ruptured nuclei and 60 confined cells with ruptured nuclei were scored per condition per experiment.
(B) Quantification of nuclear area of DCIS cells embedded into 3D collagen following the indicated confinement heights. Graph, box and whisker plot showing the median value and 10-90 percentiles of nuclear projected area. Data represents 3 independent experiments where 70 cells per condition per experiment were analyzed. (C) DCIS cells stably expressing life actin-EGFP were confined for 2 h at the indicated heights. Following the confinement period, cells were harvested and embedded in 3D type I collagen and imaged for 15 h. Bar, 20 µm. (D) Scatter dot plots of different measured parameters while cells where embedded into 3D collagen (lines are median). Data represents 2 independent experiments where 25 cells per condition per experiment were analyzed. (E) DCIS cells were transiently depleted for TREX1 or cGAS using siRNA and 48 h later cells were pre-treated for 12 h with doxorubicin (Doxo, 80 nM) or DMSO (vehicle).
(J) Graph showing the rate of in situ tumor burden (period of 6-8 weeks post-intraductal injection). Data represents the mean ± SEM. A total of 4 parental and 11 TREX1 KO glands were analyzed. Figure 3 Condi�on:     Table  S1  (relative to figure  3D ). Testing for differences in the observed distributions of TREX1-mCherry labelled by gold particles between two groups of cells (not confined and 2µm confined), estimating observed and expected numbers of gold particles in a given compartment. For a particular compartment in a given cell, the number of expected gold particles was calculated by multiplying the corresponding column sum by the corresponding row sum and then dividing by the grand row sum. The compartment "Cytoplasm" also includes the gold particles associated to membranes such as ER and multivesicular bodies.
de Gennes-IPGG (Equipement d'Excellence, "Investissements d'avenir", program ANR-10-EQPX-34) and laboratoire d'excellence, "Investissements d'avenir" program ANR-10-IDEX-0001-02 PSL and ANR-10-LABX-31; Laboratory of Excellence (LabEx) CelTisPhyBio ANR 11-LABX-0038 to SAG; Plan Cancer 2018 'Single Cells' (19CS007-00) to FR; Fondation pour la Recherche Médicale (FDT20160435078) to CC; Associazione Italiana per la Ricerca sul Cancro (AIRC-IG#18621 to GS, AIRC-0IG#22145 to CT, and 5XMille #22759 to GS and CT); the Italian Ministry of University and Scientific Research (MIUR) to GS (PRIN: Progetti di Ricerca di Rilevante Interese Nazionale -Bando 2017#2017HWTP2K); Institut National du Cancer INCA_7905 (MetaCLEM) and INCA PLBIO 2019-1-PL BIO-07-ICR-1, INSERM Plan Cancer Single Cell grant 19CS007-00, Fondation ARC pour la Recherche contre le Cancer (PGA1 RF20170205408) and institutional funding from Institut Curie and Centre National de la Recherche Scientifique to PC; ERCadg CellO (FP7-IDEAS-ERC-321107) to JLV; LABEX DCBIOL (ANR-10-IDEX-0001-02 PSL and ANR-11-LABX-0043), ANR ANR-17-CE15-0025-01 and ANR-18-CE92-0022-01 to NM; INCA PLBIO 2019-1-PL BIO-07-ICR-1, INSERM Plan Cancer Single Cell grant 19CS007-00 to MP, NM and PC, Italian Foundation for Cancer Research (AIRC) Accelerator Award ID.
Bars, 5 µm and 1 µm (magnification). Images are representative of 2 independent experiments. (J) Quantification of DNA damage levels (number of 53BP1 foci) in DCIS wt cells and DCIS TREX1 KO #1 cells stably expressing 53BP1-mCherry reconstituted with the indicated TREX1 constructs fused to GFP and confined at 2 µm. Quantification was performed in cells displaying NE rupture. Data represents the mean ± SD of 3 independent experiments where 25 cells per condition per experiment were analyzed. (K) Images showing.
Data was normalized by the mean of control samples. Data represents 3 independent experiments where 50 cells per condition per experiment were analyzed. (B) Quantification of nuclear area of DCIS cells embedded into 3D collagen following the indicated confinement heights. Graph, box and whisker plot showing the median value and 10-90 percentiles of nuclear projected area. Data represents 3 independent experiments where 70 cells per condition per experiment were analyzed. (C) DCIS cells stably expressing life actin-EGFP were confined for 2 h at the indicated heights. Following the confinement period, cells were harvested and embedded in 3D type I collagen and imaged for 15 h. Bar, 20 µm. (D) Scatter dot plots of different measured parameters while cells where embedded into 3D collagen (lines are median).
Acknowledgements: We thank the flow cytometry and cell sorting facility, the animal facility at the  Institut Curie  and the microscopy facility of  IPGG  for equipment and technical assistance. We thank  Titia de Lange  and  John Maciejowski  for providing the guideRNA to generate the TREX1 KO CRISPR cell lines,  John Maciejowski ,  Alexis Lomakin  and  Ana-Maria Lennon-Dumenil , for suggestions during the writing of this manuscript,  Nicolas Carpi  for lab management, plasmid amplifications and computer technical support,  Juan-Manuel Garcia Arcos  for help in assembling the figures.
Participants Eighteen participants in the age range 19-35 years were recruited from the MRC CBU's volunteer panel database (9 identified themselves as female, mean age = 28. 33). Two of them had to be excluded because we were not able to obtain their structural MRI images, and one was excluded because of large MEG artefacts during the recording session (likely due to a dental implant), leaving 15 datasets for the final analysis. All participants reported to be right-handed native English speakers, to have normal or corrected-to-normal vision, and to have no history of neurological or developmental disorders. They were monetarily reimbursed for their participation to the study. This study was approved by the Cambridge Psychology Research Ethics Committee.
Stimuli Our stimuli and experimental settings are close to those described in  Rossion et al (2015). The stimulus set was the same as used in that study. It consisted of 200 black and white photographic images of various common objects (animals, plants, objects and houses) collected from the internet, and 50 photographs of unfamiliar faces. Importantly, all objects and faces were unsegmented, i. e. , embedded in their original visual scene. The various objects and faces were centred, but they differed in terms of size, viewpoint, lighting conditions, and background ( Fig. 1  ; Movie 1). The entire set of stimuli is available online at https://face-categorization-lab. webnode. com/resources/naturalface-stimuli/. The stimuli were converted to grayscale, resized to 200 × 200 pixels, and equalized in terms of pixel luminance and rootmean square contrast in Matlab.
Data acquisition EEG/MEG data were acquired on an Elekta Neuromag Vectorview system (Elekta AB, Stockholm, Sweden), containing 306 MEG sensors (102 magnetometers and 204 gradiometers), and 70 EEG electrodes mounted on an Easycap cap (EasyCap GmbH, Herrsching, Germany). The EEG recording reference electrode was attached to the nose, and the ground electrode to the left cheek. The electrooculogram (EOG) was recorded from electrodes above and below the left eye (vertical EOG) and at the outer canthi (horizontal EOG). The sampling rate during data acquisition was 1000 Hz and an on-line band pass filter 0. 03 to 330 Hz was applied. Prior to the EEG/MEG recording, the positions of 5 Head Position Indicator (HPI) coils were attached to the EEG cap (for head localisation inside the scanner and continuous movement tracking), 3 anatomical landmark points (two preauricular points and nasion) as well as the EEG electrodes and about 50-100 additional points that cover most of the scalp were digitised using a 3Space Isotrak II System (Polhemus, Colchester, Vermont, USA) for later co-registration with MRI data.
Our data can be made available via the MRC Cognition and Brain Sciences Unit's data repository on request. High-resolution structural T1-weighted MRI images were acquired in a 3T Siemens Tim Trio scanner at the MRC Cognition and Brain Sciences Unit (UK) with a 3-D magnetization prepared rapid gradientecho sequence, field of view = 256 × 240 × 160 mm, matrix dimensions = 256 × 240 × 160, 1 mm isotropic resolution, repetition time = 2250 ms, inversion time = 900 ms, echo time = 2. 99 ms, and flip angle = 9°.
Pre-processing MEG data were subjected to spatio-temporal signal-space separation (SSS) implemented in the Maxfilter software (Version 2. 2. 12) of Elekta Neuromag to remove noise generated from sources distant to the sensor array  ( Taulu and Kajola, 2005 ; Taulu and Simola, 2006 ). The SSS procedure included movement compensation (locations recorded every 200 ms), bad MEG channel interpolation, and temporal SSS extension (with default buffer length 10 s and sub-space correlation limit 0. 98). The origin in the head frame is chosen as (0,0,45) mm. The following steps of analysis were performed in MNE-Python Version 0. 20 software package ( http://martinos. org/mne/stable/index. html )  ( Gramfort et al. , 2013 ). After visual inspection of the raw data, bad EEG channels (determined by visual inspection) were interpolated using spherical harmonics and 'accurate' option in MNE-Python.
On average we interpolated 1. 8 EEG channels and 2. 3 MEG channels (as above) per participant. A notch filter at 50 and 100 Hz was then applied, followed by a band-pass filter between 0. 1 and 140 Hz. In order to remove eye movements and heart artefact an Independent Component Analysis (ICA) was computed, removing a maximum of 4 ICA components (2 for heart, 2 for eyes). The exact ICA procedure closely followed the examples provided for the MNE-Python software ( https://martinos. org/mne/dev/auto_tutorials/plot_ica_from_raw. html ), which uses the temporal correlation between ICA components and EOG channels as a criterion for the removal of ICA components.
g. with respect to laterality), and that their combination provides the most complete information for source estimation  ( Ahlfors et al. , 2010 ; Hauk et al. , 2019 ; Henson et al. , 2009 ; Molins et al. , 2008 ). The EEG measures the electric potential at the locations of individual electrodes in Volts. The MEG data in our study were measured by two sensor types of the ME-GIN Vectorview system: magnetometers and planar gradiometers. Magnetometers measure the magnetic flux through a single coil in Tesla, while gradiometers measure the magnetic flux gradient (i. e. the difference between two adjacent coils) in two orthogonal directions in Tesla-Per-Meter. A magnetometer and two orthogonal planar gradiometers at the same location are sensitive to different source configuration (they have orthogonal "leadfields ").
Source space analysis Source estimation was performed on combined EEG/MEG data using L2-minimum-norm estimation, as appropriate for data where the number and location of sources is not known a-priori  ( Hämäläinen and Ilmoniemi, 1984 ; Hauk, 2004 ). In the frequency domain, source estimates were computed for the summed topographies across harmonics for the face-selective and base frequencies, respectively. This was performed in MNE-Python software with standard parameters settings. We used individual MRI images for head-modelling. The MRI data were pre-processed in Freesurfer V6. 0. 0  ( Fischl, 2012 ) , and the head model (3-layer boundary element model) created in MNE-Python. We used L2-minimum-norm estimation without depth-weighting or noisenormalisation, and with a loose orientation constraint (ratio of variances between tangential and normal dipole components: 0.
2). The data from different sensor types were combined for source estimating using a standard "whitening " approach implemented in the MNE-Python software. We used a regularisation parameter based on a SNR value of 3 (default in the software). The procedures for baseline-correction and z-scoring in the frequency domains as described above were also applied to sourcespace results. The same parameter settings were applied for source estimation of the evoked face-selective signals in the time domain. The laterality of face-selective FPVS responses in source space was investigated for several regions-of-interest in posterior inferior temporal cortex from the HCP-MMP1. 0 parcellation (Human Connectome Project Multi-Modal Parcellation version 1. 0)  ( Glasser et al. , 2016 ). While the "fusiform face complex " of this parcellation is of particular interest for our study, we also included surrounding regions due to the limited spatial resolution of EEG/MEG (see Fig.
g. Grill-Spector et al. 2017 , Jonas et al. 2016 , Kanwisher et al. 1997 , Rossion et al. 2012 and Sergent and Signoret 1992 )  and most importantly with intracerebral recordings obtained with the exact same stimulation paradigm  ( Hagen et al. , 2020 ; Jonas et al. , 2016 ). The sources for evoked responses in the time domain showed recurrent activation patterns within the first 250 ms after stimulus onset. All of our individual participants except one in MEG showed significant face-selective responses. We obtained our results based on data acquired in a very short amount of time (about three minutes). Thus, the FPVS paradigm can be used as an efficient localizer for face categorization brain activity in both EEG and MEG. Since their sensors are sensitive to different source distributions (i.
DNA extraction and library preparation We sequenced whole genomes of the two pools of recovered (Experiment 1: 116 worms / Experiment 2: 137 worms) and susceptible worms (Experiment 1: 116 worms / Experiment 2: 137 worms) and measured allele frequencies in each pool to identify genome regions showing high differentiation. We extracted gDNA from pools of worms (Blood and Tissue kit, Qiagen) and prepared whole genome libraries in triplicate (KAPA HyperPlus kit, KAPA Biosystems). Raw sequence data are available at the NCBI Sequence Read Archive (PRJNA699326).
a. Sequence analysis and variant calling We (i) aligned the sequencing data against the S. mansoni reference genome using BWA  (67)  and SAMtools  (68) , (ii) used GATK  (69, 70)  to mark PCR duplicates and recalibrate base scores, (iii) used the HaplotypeCaller module of GATK to call variants (SNP/indel) and GenotypeGVCFs module to perform joint genotyping. We merged VCF files using the MergeVcfs module. All steps were automated using Snakemake (71).
gDNA extraction and library preparation and bioinformatics We recovered the F1 SmLE-PZQ-ER and SmLE-PZQ-ES worms and extracted gDNA from pools of adult males or females separately. We prepared whole genome libraries from these pools in triplicate using the KAPA HyperPlus kit (KAPA Biosystems). Sequence data are available at the NCBI Sequence Read Archive (accession numbers PRJNA701978). The analysis was identical to the GWAS and QTL mapping analysis (see Jupyter notebook and scripts (https://github. com/fdchevalier/PZQ-R_DNA)).
Variants identification in exome-sequenced data from natural S. mansoni parasites We utilized exome sequence data from S. mansoni from Africa, South America and the middle East to investigate variation in Sm. TRPMPZQ. African miracidia were from the Schistosomiasis collection at the Natural History Museum (SCAN)  (73) , Brazilian miracidia and Omani cercariae and adult worms were collected previously. We have previously described exome sequencing methods for S. mansoni  (51, 52). Data were analyzed the same way as described in Chevalier et al. (52). Code is available in Jupyter notebook and scripts associated (https://github. com/fdchevalier/PZQ-R_field).
Sanger re-sequencing to confirm the presence of the Sm. TRPMPZQ field variants To confirm the presence of variants in Sm. TRPMPZQ (when read depth was <10 reads), we performed Sanger re-sequencing of mutations of interest close to the PZQ binding site  (21). Primers and conditions are listed in Table  S4. We scored variants using PolyPhred software (v6. 18) (Nickerson et al. , 1997). Sequences are deposited in GenBank (KU951903-KU952091).
Statistical analysis Statistical analyzes and graphs were performed using R software (v3. 5. 1)  (74). We used the drc package from R to analyze dose-response datasets and Readqpcr and Normqpcr packages to analyze RT-qPCR datasets. For non-normal data (Shapiro test, p < 0. 05), we used Chi-square test, Kruskal-Wallis test followed by pairwise Wilcoxon-Mann-Whitney post-hoc test or a Wilcoxon-Mann-Whitney test. For normal data, we used one-way ANOVA or a pairwise comparison Welsh t-test. The confidence intervals were set to 95% and p-values < 0. 05 were considered significant.
a. Sequence analysis and variant calling: We aligned the sequencing data against the S. mansoni reference genome (schistosoma_mansoni. PRJEA36577. WBPS14) using BWA (v0. 7. 17) (67) and SAMtools (v1. 10)  (68). We used GATK (v4. 1. 8. 1)  (69, 70)  to mark PCR duplicates and recalibrate base scores. We used the HaplotypeCaller module of GATK to call variants (SNP/indel) and the GenotypeGVCFs module to perform a joint genotyping on each chromosome or unassembled scaffolds. We merged VCF files using the MergeVcfs module. All these steps were automatized using Snakemake (v5. 14. 0)  (71). b. QTL identification: We expect the genome region underlying resistance to be enriched in variants from high lactate producing worms. To evaluate statistical evidence for such enrichment, we examined the difference in allele frequencies between low and high lactate parasites across the genome by calculating a -score at each bi-allelic site.
gDNA extraction and library preparation SmLE-PZQ-ER and SmLE-PZQ-ES parasite populations were maintained in our laboratory. We recovered the F1 worms from each populations and extract gDNA from pools of adult males and females separately as described above. We prepared whole genome libraries from these pools in triplicate using the KAPA HyperPlus kit (KAPA Biosystems) as described (see Whole genome library preparation and sequencing). We sequenced the libraries on a HiSeq X sequencer (Illumina) using 150 bp paired-end reads. Sequence data are available at the NCBI Sequence Read Archive (accession numbers PRJNA701978).
b. RNAseq library preparation We prepared RNAseq libraries using the KAPA Stranded mRNA-seq kit (KAPA Biosystems) using 500ng RNA diluted in 50uL Tris-HCl (pH 8. 0) for each library. We fragmented mRNA (6 min 94⁰C), indexed libraries using 3'-dTMP adapters (7µM, 1 hour at 20⁰C), and used 6 PCR cycles for post-ligation library amplification. We quantified indexed libraries by qPCR (KAPA library quantification kit (KAPA Biosystems)) and assessed their fragment size distribution by TapeStation (Agilent). We sequenced the libraries on a HiSeq 4000 sequencer (Illumina) using 150 bp pair-end reads, pooling 12 RNAseq libraries/lane. Raw sequence data are available at the NCBI Sequence Read Archive under accession numbers PRJNA704646.