dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Sources and Processing Cancer estimation in GBD 2019 used 929 193 cancer-, location-, and year-specific sources of data, of which 767 514 (82. 6%) were from vital registration systems, 155 542 (16. 7%) from cancer registries, and 6137 (0. 7%) from verbal autopsy reports (eTable 1 in the Supplement). The cancers presented in this analysis include malignant neoplasms or cancer as defined by the International Statistical Classification of Diseases and Related Health Problems, Ninth Revision (ICD-9)  codes 140 to 209,  28  or Tenth Revision (ICD-10) codes C00 to C96. 29  Incidence and mortality data with these ICD codes are mapped to GBD cancer causes 2 0 (eAppendix and eTables 3-5 in the Supplement). One processing update for GBD 2019 was the remapping of deaths coded to ICD-10 code C22.
9; because this code includes unspecified primary or secondary liver cancer, a subset of these deaths were redistributed to various other cancers that metastasize to the liver. 20, 30, 31 Kaposi sarcoma was not estimated because deaths were primarily redistributed to be of HIV/AIDS (eAppendix in the Supplement). 20 The GBD NMSC estimates included squamous cell carcinoma and basal cell carcinoma. Because NMSC reporting was incomplete in many cancer registries,  32  GBD 2019 additionally incorporated data from the literature and clinical sources to estimate NMSC burden (eAppendix in the Supplement).
Modeling Process The GBD cancer mortality and YLL estimation process included 2 primary steps (eFigure 2 in the Supplement), beginning with the estimation of cancer MIRs, which provide an association between mortality and incidence estimation, maximizing data availability. The MIRs were modeled using a space-time Gaussian process regression approach  26  (MIR methods are described in the eAppendix in the Supplement) using matched incidence and mortality data from cancer registries (eTable 6 in the Supplement) and the GBD-estimated health care access and quality index 33 as a covariate. These estimated MIRs were then used to convert cancer registry incidence data into inputs for mortality modeling. Estimating cancer mortality was the second step. The GBD 2019 study used a Cause of Death Ensemble model (CODEm) approach that combined data from vital registration systems, cancer registries, and verbal autopsy reports to estimate mortality across several submodels.
Each sequela prevalence was multiplied by a sequelaspecific disability weight that represented the magnitude of health loss (eTable 12 in the Supplement). 20 For 5 cancer types (bladder, breast, colorectal, larynx, and prostate cancer), the total prevalence additionally included lifetime prevalence of procedure-related disability (eg, laryngectomy due to larynx cancer). These procedure-related prevalence estimates were modeled in the Bayesian meta-regression tool DisMod-MR, version 2. 1, 20 using medical records data on the proportion of patients with cancer who underwent these procedures and the estimated number of 10-year survivors (eAppendix in the Supplement). These procedure-related prevalence estimates were then multiplied by procedure-specific disability weights (eTable 12 in the Supplement).
healthdata. org/gbdcompare/ and http://ghdx. healthdata. org/gbd-results-tool). Data processing and analyses were conducted using Python, version 3. 7. 0 (Python Software Foundation); Stata, version 15. 1 (StataCorp); and R, version 3. 4. 1 (R Foundation). Code is available at https://ghdx. healthdata. org/gbd-2019/ code.
Limitations Several limitations provide opportunities for improvement in future GBD iterations. An ongoing challenge is a lack of high-quality data in many locations. This includes time lags in data availability, nonspecific cause of death data from vital registration systems, and ascertainment limitations of verbal autopsy reports. The GBD addresses these data limitations through data-seeking efforts, data processing corrections, and modeling approaches that incorporate geospatial and temporal smoothing. These approaches allow the estimation of comprehensive results with appropriate uncertainty bounds. However, in years or locations where data were not available, estimates relied on covariates and modeling parameters, which may overestimate or underestimate true cancer burden. As data can be less available or reliable in locations within the lower SDI quintiles,  19  estimates should be interpreted with some caution.
Radiological features & tumor burden The CT-scans were analyzed by an expert radiologist who annotated all lesions in chest-abdomen-pelvis scans using an internal annotation software called Spyd. The annotations were performed with the following constraints: a) Precise freehand circumscribed annotation of all lesions in the body at their largest axial position (single slice), b) Lymph nodes were only considered a lesion if their smallest axis measured more than 10 mm, c) all pulmonary micronodules measuring more than 3 mm were annotated, d) Each annotation contained a label describing the location of the tumor. The tumor labels used were heart, lung, liver, lymph node, bone, spleen, kidney, carcinosis, ovary, pancreas, skin/soft tissue, adrenal, brain, muscle, bowels and other. The scans at both evaluations were annotated independently for each patient (Figure  1 ).
These annotations allowed us to compute the shape features: Major Axis, Minor Axis and Mesh Surface, using Pyradiomics software (29). The surface is computed using the following formulas: A = S 1 2 Oa i ÂOb i where O i a i and O i b i are edges of the ith triangle in the mesh, formed by vertices a i , b i of the perimeter and the origin O. The Major Axis and Minor Axis are computed using the two smallest singular values on the ROI-enclosed ellipsoid: major = 4 ffiffiffiffiffiffiffiffiffiffiffi l major q and minor = 4 ffiffiffiffiffiffiffiffiffiffiffi ffi l minor p :The approximate 3D volume for each metastasis was obtained with the following formula: Tumor volume = 2/3 x Surface x Minor Axis. The total tumor volume for each patient is computed using the sum of the approximate 3D volume of each metastasis.
Genome sequencing and identification of SNPs Whole-genome sequencing was performed with the Illumina MiSeq System at the IMM Transcriptomic Platform. Sequencing samples were prepared using the Illumina Nextera XT DNA Library Preparation Kit according to the manufacturer's instructions. Sequence reads were aligned with Unipro Ugene software  [43]  using the NCBI M. xanthus DK1622 genome (Gen-Bank assembly accession no. GCA_000012685. 1) as a reference. SNPs and nucleotide deletions/insertions were analyzed with Unipro Ugene software  [43]. Genetic variations were confirmed by Sanger sequencing (Eurofins GATC-Biotech).
The spectrophotometer was directly blanked at 585 nm (the absorption peak for Trypan Blue) using the cell-free "TPM + Trypan Blue" sample to calibrate the baseline absorbance corresponding to no retention of Trypan Blue dye by cells. Absorbance values at 585 nm (A585) were obtained for each clarified supernatant and normalized as a percentage of the WT A585 reading (i) as an internal control for each individual experiment, and (ii) to facilitate comparison of datasets across multiple biological replicates. Negative final values are due to trace amounts of cell debris detected at 585 nm in individual samples in which absolutely no binding of Trypan Blue occurred.
For all the reactions, qPCR conditions were identical and performed on a CFX96 Real-Time System (Bio-Rad). The reaction volume was 15 μL and the final concentration of each primer was 0. 5 μM. The qPCR cycling parameters were 95˚C for 2 min, followed by 45 cycles of 95˚C for 5 s, 55˚C for 60 s. A final melting curve from 65˚C to 95˚C was added to determine the specificity of the amplification. To determine the amplification kinetics of each product, the fluorescence derived from the incorporation of BRYT Green Dye into the double-stranded PCR products was measured at the end of each cycle using the GoTaq qPCR Master Mix 2X Kit (Promega). The data were analysed using Software Bio-Rad CFX manager 3. 0 (BioRad), and the delta Ct method. The 16s rRNA and constitutively expressed frzE genes were used as a reference for normalization.
Acknowledgments We would like to thank  Vladimir Pelicic ,  RomeVoulhoux ,  Dorothee Murat  and  Julien Herrou  and for helpful discussion and critical reading of the manuscript;  Julien Herrou  and  Deborah Byrne  for their great help with PilY1. 1 purification;  Hugo Le Guenno  for helping with the TIRF microscopy;  Yann Denis  and  Romain Barre  ´for their help with whole genome sequencing and performing RNAseq.
The ELISA assay was carried out as previously described  [29]  using the anti-mouse-IgG secondary antibody conjugated to horseradish peroxidase. Each plate was read and analysed using a specific microarray reader which acquires highresolution digital images. An integrated software calculates the pixel intensity for each spot with the background noise subtracted. We calculated the mean value of the duplicated spots to get the net intensity of each antigen. The data were incorporated into a statistical analysis package and analysed accordingly.
Statistical analysis Linear Mixed Model (LMM) analysis was used for the statistical analysis of the longitudinal data, since a mixed effects model has both random and fixed effects. In the current model, treatment group is a fixed effect, while time and mouse are random effects. Longitudinal data are described by the response variable (biomarker intensity), which was repeatedly measured at each group and time. The data prior to start of treatment were disregarded from the LMM analysis. There were no missing value observations. Response variables were the antigen reactivities of 16 biomarkers in the Multi-Cruzi assay and the log-transformed bioluminescence total flux. Independent variables were time (in days after infection) and treatment group (vehicle; benznidazole 30 mg kg -1 ; benznidazole 100 mg kg -1 ).
Variance and co-variance were modelled as unstructured (UN). For each subgroup, and for each antigen or bioluminescence variable, slopes and intercepts were calculated and compared. SAS Proc Mixed was used for LMM analysis, from SAS 9. 4 (SAS Institute Inc. , Cary, NC, USA). The mixed effects model allows the average intercept and slope to be fitted as fixed effects, while taking account of differences between mice and time (random effects).
Mass estimation Mass estimation is the current bottleneck of cluster number count cosmology. For future surveys, we need to construct robust mass proxies with well understood properties. In the case of Euclid, the weak lensing measurements will provide the data required to estimate the dark matter halo mass. However, weak lensing will not be efficient at detecting a large number of clusters in the Euclid dataset. Indeed, clusters are primarily detected (in the optical) as overdensities of galaxies. The Euclid consortium has selected  [6]  two algorithms for future cluster detection: PZWave  [7]  and AMICO  [8]. Both algorithms provide at least one natural mass proxy as an output. This observed quantity is the one primarily related to the selection function. The weak lensing masses serve to calibrate its relation with the true halo mass.
Simulation and mock generation The impact of the systematics that we will have to face must be measured at the precision of a Euclid-like survey, which means 15, 000deg 2 , z ∈ [0, 2], reliable lensing information and baryonic physics correctly incorporated. This represents an enormous volume, and currently, no dataset possesses these characteristics. To tackle this issue, we have chosen to use three different simulations, each one having the required properties that we are looking for. The "RayGalGroup" simulations  [12] , are used to derive the lensing properties. For the selection function, and the performance of the detection algorithm, we refer to the the results of the Cluster Finding Challenge 4. Finally, we used the Flagship dark matter halo catalog  [13]  Table  1 : Best fit value of the lensing scatter parameters introduced in equation 8, with the 1σ errors.
Note that although it is present in our table, the intrinsic scatter is not fitted, but fixed. As expected, the value of the power β is close to the intuitive value -2, as the errors should be proportional to the inverse of the angular area covered by the cluster. as a baseline to reconstruct a mock survey that is as close as possible from a Euclid-like dataset. The "RayGalGroup" sims are a set of N-body simulations that are dedicated to raytracing. The main interest of these simulations is their spacial resolution (5 h -1 Mpc), and the excellence of the lensing calculations. We use these simulations to test and constrain the relation on the scatter of the lensing mass estimates introduced with equation 8. Precisely, we fit the normalization and the power law of the relation. In this study, we fix the value of the intrinsic scatter σ int to  [14].
Selection of Candidate Genes To identify suitable target genes for targeted DNA methylation epimutagenesis the following criteria were established: 1) Candidate genes were selected from known homeobox genes in Haliotis asinine  (Hinman et al. , 2003) , homologues of genes in B. glabrata were identified by BLAST searches in the NCBI nucleotide collection database. Homologues of homeobox genes were used to be sure they were expressed during embryogenesis. 2) The DNA methylation level of the selected genes was visually examined using our reference methylome (https://zenodo. org/record/4277533) in the IGV-Integrative Genomics Viewer (http://software. broadinstitute. org/software/igv). The criteria to select a candidate gene was that it must be unmethylated. 3) The tissue expression of each gene was observed in a local RNA-Seq database to select those genes that are expressed in most tissues or at least in tissues that are easily accessible and, therefore, easy to manipulate (DNA/RNA extraction).
Elaboration of gRNAs The homeobox Nkx 2. 5-like gene was selected, a homologue of the Has-Hox4 of the gastropod Haliotis asinine (GenBank: AAK11240. 1), whose expression is restricted to the forming pleural ganglia and nerve cords, suggesting a role in the nervous system development  (Iijima et al. , 2006). Three gRNAs targeting DNA methylation canyon on the target locus: LGUN_random_Scaffold680: 40871-42922 on B. glabrata gene database (https://legacy. www. vectorbase. org) were elaborated by a software for designing CRISPR/Cas9 guide RNAs called CHOPCHOP  (Montague et al. , 2014). The off-target was also predicted against B. glabrata genome by CHOPCHOP as previously described  (Coelho et al. , 2020)  and following considerations for dCas9-DNMT3 system described in  (Brezgin et al. , 2019).
Bioinformatics and Statistics Raw reads were trimmed using Trim galore default parameters to remove Illumina adapters, then trimmed reads were mapped to the reference genome of B. glabrata with the alignment algorithm BWA-MEM that map long reads (>100 pb) to verify that the targeted gene was sequenced. After that, trimmed reads were used as input in the command line version of the software pipeline CRISPResso2 available at https://github. com/pinellolab/CRISPResso2. This pipeline allows us to align reads to a reference sequence, and to obtain conversion of target bases around the sgRNA and in the entire amplicon which allows us to calculate the conversion percentage of cytosines transformed to thymines in the target CpG sites  (Clement et al. , 2019). Nucleotide frequency of the entire amplicon was obtained from this pipeline and CpG sites were searched manually to calculate the cytosine to thymine conversion.
Targeted CpG Hypermethylation of 3. 4-76% in 50% of Transfected Snails in up to 5 CpG Sites After having established a time window in which both dCas9 and DNMT were expressed in the developing snail embryos, injections of guide RNA were performed. To verify the absence of mutations, the targeted locus was sequenced by Sanger methodology and to verify methylation level at the target gene, bisulfite Sanger sequencing was done. The target gene was not methylated at any of the 11 CpG sites (data not shown). All three sgRNAs matched to the target sequence and are located at different positions (Figure  6 ). Cleavage efficiency was assayed in vitro and sgRNA3 was shown to be the unique tested guide able to direct Cas9 to cut the targeted locus at the PAM sequence (Figure  7 ). Therefore, positive transfected snails were screened under the microscope at 72 h using fluorescence of the GFP and BFP from the plasmids.
ACKNOWLEDGMENTS We are very grateful to  M. Goodell  and her collaborators for the dCas9-SunTag-DNMT3A containing plasmids and to  J. L. Lopez  for helping in the elaboration of sgRNAs. We thank  T. G. Montague  for including B. glabrata genome in the web tool CHOPCHOP. We are grateful to  J. P Concordet  and  A. de Cian  from  MNHN Paris and Celphedia/Te  for providing recombinant Cas9 and help with the in vitro efficiency assays of sgRNAs. We thank  A. Rognon  for helping in the microinjection tests and for her help in the video guide protocol. We are grateful to  C. Nevoit  from  Transgenesis core facility  of  UMS006  for her help in the optimization of the microinjection technique. We thank  A. Modat  and  J. Fabryka  for their help in the elaboration of the video of the microinjection protocol.
FUNDING This work was supported by  Wellcome Trust   strategic award  ( 107475/Z/15/Z ) and by a PhD grant to NL from the  Region Occitanie  ( EPIPARA  project) and the  University of Perpignan  Via Domitia graduate school ED305. With the support of  LabEx CeMEB , an  ANR  " Investissements d'avenir" program  ( ANR-10-LABX-04-01 ) through the  Environmental Epigenomics Platform and the "projets de recherche exploratoires du CeMEB 2018 " project " Epigenetics of inbreeding depression (EPID) ". This study is set within the framework of the  "Laboratoires d'Excellences (LABEX) "  TULIP  ( ANR-10-LABX-41 ). BQR  UPVD  provided funding for the Nanoject III Programmable Nanoliter Injector.
DATA AVAILABILITY STATEMENT The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www. ncbi. nlm. nih. gov/, PRJNA770996. https://doi. org/10. 5281/zenodo. 5786326, 10. 5281/zenodo. 5786326. https://doi. org/10. 5281/ zenodo. 5786314, 10. 5281/zenodo. 5786314. https://doi. org/10. 5281/zenodo. 4277533, 10. 5281/zenodo. 4277533.
Nanodroplet Characterization The particle size distribution data were obtained using a Dynamic Light Scattering (DLS) apparatus (Zeta Sizer Nano-S, Malvern Instruments, Orsay, France). Sample preparation was achieved by diluting the initial solution with deionized water by 10-times. These samples were placed in a 45 µL quartz tank and subjected to 6 measurements of 15 s each at 25 • C with a scattering angle of 173 •. Refractive indexes assigned to PFOB and water (dispersant) were defined as 1. 305 and 1. 333, respectively. Aside from droplet optimization experiments, all DLS measurements were performed in triplicate. The hydrodynamic droplet mean diameter Dh is an average of 18 measurements and is reported as an intensity-based distribution with the cumulant method (Z-average).
Transmission Electron Microscopy (TEM) Images TEM observations were conducted with a MET Hitachi 7800-120 kV instrument. A drop of emulsion was covered with a carbon-coated copper grid and allowed to equilibrate for 2 min. The grid was then stained with a 3% ammonium molybdate solution. Excess liquid was removed by gently touching the edge of grid with filter paper and the grid dried overnight at room temperature. After that, the grid was transferred to TEM operating at an acceleration voltage of 80 kV. 19 F-Nuclear Magnetic Resonance ( 19 F-NMR) All spectra were recorded on a 400 MHz Bruker Avance II spectrometer with a resonance frequency of 376. 53 MHz for 19 F-NMR. Spectra were acquired using the inverse-gated decoupling technique. Each spectrum was the result of 256 scans with 131 072 data points using a relaxation delay of 4 s.
Cells were imaged using a Leica TCS SP5 CLSM (Leica Microsystems, Nanterre, France) equipped with an argon laser for green labeling (FITC, excitation 488 nm) and a helium-neon laser for red and blue labeling (DsRed, excitation 543 nm and DiD, excitation 633 nm, respectively). A Z-stack acquisition of 20 frames covering a depth of 12 µm was recorded for evaluation of the nanodroplet distribution across the section. Image analysis was performed using ImageJ software (NIH, Bethesda, MD, USA). 2. 17. Fluorescence-Activated Cell Sorting (FACS) Cells (12,500 cells cm -2 for HMEC-1, 37,500 cells cm -2 for C8-D1A and 12,500 cellscm -2 for U87-MG) were seeded on 6-well plates. After 24 h (U87-MG and HMEC-1) or 72 h (C8-D1A) of growth at 37 • C, cells were exposed with fresh medium alone (control cells) or containing DTE (300 µL, emulsion at 2.
6% of Fv, diluted to 1/50) for 1, 2, 4, 10 and 24 h. After exposure, the suspension was removed and cells were rinsed three times with PBS. After being detached with trypsin (300 µL), cells were washed then fixed for 10 min at room temperature with a mixture of 50% of 4% PFA solution and 50% of buffer solution containing PBS, 0. 5% of BSA and 2 mM of EDTA and redispersed in the same buffer solution. Semi-quantitative studies were performed using flow cytometry (FACScalibur TM , BD Biosciences, Franklin Lakes, NJ, USA). A total of 10,000 events were counted per sample and data were recorded with CellQuest Pro Software (BD Biosciences, Franklin Lakes, NJ, USA) and analyzed using Flowing Software 2. The percentage of fluorescence doubly positive cells per 10,000 cells and the mean fluorescence intensity (MFI) of 10,000 treated cells were reported for each exposure time and were statistically (Student's t-test) compared with the previous exposure time for each cell line.
1 mL per 10 g of weight) and sacrificed for blood collection. The whole blood was collected in heparincoated tubes by cardiac puncture. For 7 pre-determined time points (1, 8, 24, 48, 72 h, 7 and 14 days) major organs (liver, spleen, kidneys, brain, lungs and pancreas) were also harvested after mice perfusion with PBS. Organs were weighed and homogenized in PBS (100 to 600 µL depending on the organs) with a T25 Ultra-Turrax ® (IKA, Staufen, Germany). Blood and organ samples were stored at -80 • C until determination of PFOB content by 19 F-NMR. The whole blood and organ concentration-time profiles were obtained by averaging the dosage values of 5 mice per sampling time. To estimate pharmacokinetic parameters, non-compartmental analysis was performed using PKanalix software, version 2020R1 (Antony, France: Lixoft SAS, 2020).
Intracerebral Accumulation of STE Using FACS Ten minutes before FUS, 3 mice were injected i. v. (through retro-orbital route) with STE (300 µL). Mice were sacrificed 4 h post BBB disruption and perfused with NaCl 0. 9% through the left ventricle. Brains were collected and the sonicated and unsonicated hemispheres were separated; 3 areas were collected in each hemisphere and dissociated with a mouse brain dissociation kit (Miltenyi Biotec, Bergisch Gladbach, Germany). Semi-quantitative analysis was performed using flow cytometry (FACScalibur TM , BD Biosciences, Franklin Lakes, NJ, USA). In total, 10,000 events were counted per each area of each hemisphere and data were recorded with CellQuest Pro Software (BD Biosciences, Franklin Lakes, NJ, USA) and analyzed using FlowJo software (v10, Mario Roederer, Ashland, OR, USA).