dataesr/scientific-paragraphs-categorization huggingface dataset:
Planar laser-induced fluorescence (PLIF) was applied to find the position of the water subdroplet with a high degree of accuracy. As in the experiments discussed by Antonov et al. [28]  , rhodamine B was added to water (10 0 0 μg/l) prior to the preparation of the composite droplet. Since rhodamine B is not miscible in the fuel, only the water subdroplet emits a fluorescent signal when illuminated by the laser. In addition, a shadowgraphy technique using a high-speed camera provided sharp images of the droplet edge. The centroids of the water subdroplet and the fuel droplet were determined using a homemade image analysis program based on Matlab software and its image processing toolbox. Then, the shift L between the two centroids (fuel droplet and water subdroplet) was calculated as illustrated in Fig.
9. PLIF visualisation of the water subdroplet was performed before heating started. This allowed us to estimate the values of L used in the Shift Model. This shift was assumed to remain unchanged until at least the time when puffing/micro-explosion occurred. In several experiments, however, some movements of the water subdroplet could be observed, which may lead to differences between the model predictions and the results of the experiments. The observed times to puffing/micro-explosion and those predicted by the Centre Model (CM) and Shift Model (SM) for nine cases are presented in Table  1. As follows from this table, in most cases (Cases 1, 2, 3, 5, 6, 7) the values of τ p predicted by SM are much closer to the experimental data than those predicted by CM. The deviations between the predictions of SM and the experimental results could be partly attributed to considerable uncertainty in the estimation of S using our experimental data, most probably due to movement of the water subdroplet during the experiment.
Conclusions An approach to the verification of the predictions of the previously developed model of puffing/micro-explosion (see  [12]  ) is suggested. That model uses the assumption that a spherical water subdroplet is located exactly in the centre of a spherical fuel droplet. The analytical solution to the transient heat transfer equation in this droplet, with the Robin boundary condition at its surface, is implemented into a numerical code and used at each time step of the calculations. The time instant when the temperature at the fuel-water interface is equal to the water nucleation temperature is considered to be the start of puffing/micro-explosion. Verification was performed by comparing the predictions of this model with the predictions of the same model, but in which the solution to the heat conduction equation inside the composite droplet was found using the heat transfer module available from COMSOL software (a purely numerical solution to the equation).
Acknowledgments The authors are grateful: to the  Université de Lorraine  and the  Institut Carnot Icéel for the CALICO (Combustion d'Aérosols Liquides Complexes)  grant which supported  G. Castanet  (who contributed to the development of the model, the simulations and preparation of the text of the paper); for a scholarship from the  President of the Russian Federation  (Grant  SP-447. 2021. 1 )which supported  D. Antonov  (who performed the experiments, and applied the model to their analysis); to the  National Research Tomsk Polytechnic University  (project  VIU-ISHFVP-60/2019 ) which supported  P. A. Strizhak  (who contributed to the analysis of the results); and to the  Russian Science Foundation  (Grant  21-19-00876  ), which supported S. S. Sazhin  (who contributed to the development of the model, analysis of the results and preparation of the text of the paper).
For visualization of pCHMP4C at the midbody, the staining was done as described in  (13). For visualization of pppCHMP4C in ACBs, cells were pre-permeabilized and stained as described in  (51). Cells and MBRs were finally mounted in Mowiol (Calbiochem). Image acquisition was done with an inverted Eclipse Nikon Ti-E microscope, using a 100× 1. 4 numerical aperture (NA) PL-APO objective lens or a 60× 1. 4 NA PL-APO VC objective lens, coupled to a charge-coupled device (CCD) camera (Photometrics CoolSNAP HQ) or Retiga R6 CCD camera (Teledyne Photometrics) and MetaMorph software (MDS), or using a CSU-X1 spinning disk confocal scanning unit (Yokogawa) coupled to a Prime 95S scientific complementary metal-oxide semiconductor (sCMOS) camera (Teledyne Photometrics) and CSU 100× objective. Images were then converted into 8-bit images using Fiji software (National Institutes of Health) and mounted in Photoshop CS6.
Time-lapse microscopy For time-lapse phase-contrast imaging (abscission times), 30,000 cells were plated on glass-bottom 12-well plates (MatTek) 2 days before recording, incubated in an open chamber (Life Imaging) equilibrated in 5% CO 2 , and maintained at 37°C. In the case of high and low confluency conditions (Fig. 5E ), 100,000 and 10,000 trypsinized cells, respectively, were replated on glass-bottom 12-well plates for 3 hours. The culture medium was then changed, and SiR-tubulin was added for 1 hour before recording. Time-lapse sequences were recorded every 10 min for 48 hours using an inverted Nikon Eclipse Ti-E microscope with a 20× 0. 45 NA Plan Fluor ELWD controlled by MetaMorph software (Universal Imaging). For time-lapse fluorescent imaging, images were acquired using an inverted Nikon Eclipse Ti-E microscope equipped with a CSU-X1 spinning disk confocal scanning unit (Yokogawa) and with an electron-multiplying CCD (EMCCD) camera (Evolve 512 Delta, Photometrics) or a Prime 95S sCMOS camera (Teledyne Photometrics).
Images were acquired with CSU 100× or CSU 60× 1. 4 NA PL-APO VC objectives and MetaMorph software (MDS). For fig. S3B , images were acquired at 1-min intervals with Z-stacks (0. 7 m between Downloaded from https://www. science. org at Institut Pasteur on April 14, 2022 Z-planes) for 3 hours. Cavin1 dots were manually tracked with ImageJ and analyzed from the Z-planes with the highest fluorescence intensities.
Laser ablation Laser ablation experiments were performed using a pulsed 355-nm UV laser (Roper Scientific) controlled by MetaMorph and the iLas2 system (GATACA systems). The laser ablation system was connected to an inverted Nikon Eclipse Ti microscope confocal spinning disk equipped with a Yokagawa CSU-X1 spinning head, coupled to an EMCCD camera (Evolve, Photometrics) and a 100× objective (Nikon S Fluor 100× 0. 5 to 1. 3 NA). The 355-nm UV laser is sufficient for membrane and microtubule cut, as described and demonstrated in  (14). To record the movement of the midbody following laser ablation, fast acquisition movies (20-ms time lapse) were made using only transmitted light. The displacement of the midbody was then tracked using the software Icy  (78). The midbody displacement after laser ablation follows a double-exponential function as described in  (14) , and the retraction speed of the midbody was obtained from a linear regression of the first hundreds of milliseconds after ablation.
Electron microscopy Glass coverslips with synchronized cytokinetic cells were chemically fixed for 2 hours at RT in cacodylate buffer (0. 1 M, pH 7. 2) supplemented with 2. 5% (v/v) glutaraldehyde and 2% (v/v) PFA, then washed in cacodylate buffer (three times), postfixed with 2% (w/v) osmium tetroxide supplemented with 1. 5% (w/v) potassium ferrocyanide (45 min for 4°C), washed in water (three times), dehydrated in ethanol (increasing concentration from 30 to 100%), and finally embedded in Embed812/Araldite (Epon) resin as described in  (79). Microtomy of embedded cell monolayers was performed on a Reichert UltraCut S ultramicrotome (Leica Microsystems) for 70-nm-thin sections collected on formvar-coated cupper/palladium grids and then contrasted with uranyl acetate and lead citrate. Electron micrographs were acquired with a TEM (Tecnai Spirit G2; Thermo Fisher Scientific, Eindhoven, The Netherlands) equipped with a 4k CCD camera (Quemesa, EMSIS, Muenster, Germany) using ITEM software (EMSIS).
Image and data analyses All images and videos were minimally processed to adjust the intensity levels, reduce image noise, and rotate the images with Fiji software. The kymographs (Fig. 4B ) were obtained using a region of interest (ROI; straight line) along the ICB at a fixed position over the entire movie, using Icy software. The scan lines were obtained using an ROI (straight line), with a width corresponding to the bridge or EP height, along the objects of interest, using Fiji. Pixel intensity values were extracted from the EPs defined with an ROI (square) for each channel of interest, using Fiji. Illustrations were created with BioRender. com.
Statistical analyses All the plots and statistical tests were performed using GraphPad Prism software. The presented values are displayed as mean ± SD for at least three independent experiments (as indicated in the figure legends). The significance was calculated using unpaired, one-sided/two-sided t tests, as indicated. For comparing the distributions of the abscission times, a nonparametric and distributionfree Kolmogorov-Smirnov test was used. In all statistical tests, P > 0. 05 was considered as nonsignificant. P values are indicated in the figures.
MiRISC immunoprecipitation, RNA sequencing library preparation, sequencing and analysis MiRISC-immunoprecipitation was performed as previously described  (8)  with a mouse-anti-AGO2 antibody (clone E12-1C9, Abnova). Library preparation from mRNA enriched from total RNA with a NEBNext Poly(A) mRNA Magnetic Isolation Kit (NEB) and from miRISCisolated RNA was performed using a NEBNext Ultra II Directional RNA Library Prep Kit from Illumina (NEB). Library preparation from human islets total RNA (200ng) was performed using a NEBNext Low Input RNA Library Prep kit (NEB). Libraries were sequenced on a HiSeq4000, Mapping and differential expression analysis was performed with Salmon v1. 3. 0  (9)  and DESeq2  (10). For miRNA target identification, Total mRNA (T-RNA) and immunoprecipitated RNA (RIP-RNA) samples were treated as separate datasets and the two resulting gene lists of differential analysis were used to calculate the ratio of immunoprecipitated RNA to total mRNA (IP-RNA/T-RNA) for each gene.
MiR-125b targets genes encoding mitochondrial and lysosomal proteins To identify biological pathways and functions regulated by miR-125b we submitted the list of genes dysregulated at the RNA level (T-RNA, 306 and 317 genes down-and up-regulated, respectively, padj < 0. 1, Table  S1 ) as well as those identified as miR-125b direct targets with a RIP/T-RNA > 1. 5 (Table  1 ) to a Database for Annotation, Visualization and Integrated Discovery, DAVID(23). Functional annotations with high enrichment scores included mitochondrion, tricarboxylate (TCA) cycle, sterol biosynthesis, protein transport, Golgi apparatus and endosome (Fig. 2C , Table  S2 ), all closely associated to the capacity of the βcells to produce and secrete insulin in response to glucose. The gene at the top of our miR-125b-targets list, the lysosomal/Golgi-associated M6pr (Manose-6-phosphate receptor-cation dependent, M6PR-CD) controls trafficking of lysosomal hydrolases from the Golgi via endosomes  (24)  and thus may influence protein turnover and lysosomal function.
Experimental results The considered experimental test bench is shown in Fig 7. Here, a PWM-controlled threephase inverter is used to emulate the adverse grid voltage signal. The voltage sensors are used to measure the voltages at the load side. The control systems as well as the comparative techniques are implemented on a dSPACE 1104 real-time platform using 10 kHz sampling frequency. Technical details of the setup are given in Table  4. Resultsare exported to Matlab for plotting. To validate the theoretical development, three experimental tests are considered. In the first test, -2Hz frequency step is considered. From the comparative experimental results as shown in Fig  8 , one can find that both CDSC and the proposed method have roughly 1. 5 cycles convergence time while it is two times higher for QT1.
Overview of U. S. domestic migration flows The most recent ACS county-to-county flow dataset  26  reports that about 45. 6 million people migrated to the U. S. per year during the period 2015-2019, which corresponds to 14. 2% of the U. S. population 27. Approximately 43. 5 million annual moves corresponded to domestic migration (moves within the U. S. 28  ), while 2. 1 million accounted for inflows of individuals from other countries (viz. international immigration). With respect to domestic migration, 25. 7 million people per year migrated within the same county, thus showing that the highest share of domestic flows (59%) is intra-county. Annually, about 10. 4 people moved between different counties within the same state, thus intrastate flows account for 24% of the domestic migration (Supplementary Fig.
Indeed, we extended our analysis for two other time periods, 2005-2009 and 2010-2014. With respect to the spatial distribution of intra-and inter-city flows, similar trends are observed in both periods (Supplementary Figs. 12, 13), namely inter-city flows are responsible for the highest share of inflows to core counties, and intra-city flows are responsible for the highest share of inflows to external counties. We also explored the role of population density in driving netflows The robustness of our findings is checked with additional migration data from the Internal Revenue Service (IRS), which reports the year-to-year address changes on individual tax returns filled with the IRS  46. The results obtained with the analysis of IRS datasets from periods 2015-2016, 2016-2017, 2017-2018, 2018-2019 (Supplementary  Figs.
16, 17, 18, 19), reveal similar trends to those we found using ACS data. Particularly, we observe that, for all periods considered, the correlation between intra-city netflow/S and distance to core county is stronger than we found with ACS data, thus highlighting the role of intra-city flows in driving population to external regions of cities. The main difference between both datasets is in the percentage of intraand inter-city inflows and outflows: while ACS data indicates that both flows have approximately the same contribution to the total flows, the IRS data indicates that, besides the core county, intra-city flows are responsible for about 80% of inflows and outflows of metro areas.
In this context, we propose a framework for studying the spatial distribution of migratory flows. We have focused here on the U. S. because of the public availability of robust datasets at the county level, but our conclusions could be extended to other developed and highly urbanized countries where the highest share of domestic migration is composed of intra-and inter-city flows. However, the general intra-and inter-city flow patterns we reported here might not hold for low and middle-income countries presenting lower urbanization levels. For instance, city population growth in countries experiencing rapid urbanization in Asia and Sub-Saharan Africa is mainly composed of rural-to-city flows, in which international migration of refugees and the emergence of large informal settlements are also important components of urban growth  [60] [61] [62] [63] [64] [65] [66] [67].
Data collection Our analysis is focused on a five-year period, from 2015 to 2019. We used two main sources of datasets, both from the U. S. Census. The first main source is the ACS County-to-County Migration Files  26. Annually, approximately 3. 54 million independent housing units addresses were selected among all the U. S. counties. There were four modes of data collection: internet, mail, telephone, and personal visit, in which respondents are asked whether they lived in the same residence one year ago. The results, reported over 5-year periods for robust flow estimates of less populated counties, are made available cleaned and preprocessed in spreadsheet files  26. From this file, we obtain estimates of inflows and outflows between pairs of counties, in which "flow estimates resemble the annual number of movers between counties for the 5-year period data was collected"  73.
The second main source is the County Population Totals: 2010-2019 dataset  74  , which offers "population, population change, and estimated components of population change" from April 1, 2010 to July 1, 2019. Using the resident population from the 2010 Census as a starting point (population base), county population estimates are derived from the following demographic balancing equation: population estimate = population base + births -deaths + migration  74  (see  74  for detailed explanations of how births, deaths and migration are estimated). From this dataset, which is made available cleaned and preprocessed in a spreadsheet table, we obtain the domestic netflows, births and deaths for each county from July 1, 2015 to June 30, 2019 used to compute the quantity x. We focus our study on 3,141 counties and 384 U.
S. metro areas. To analyze the housing prices of origin and destination counties, we used the housing data from Zillow Research 75. Zillow publishes the Zillow Home Value Index, which reflects the typical values of homes across the U. S. The data are also made available at the county level, cleaned and preprocessed in a spreadsheet table. The IRS data we used to check the robustness of our findings were collected from the IRS-SOI Tax Stats-Migration Data website  46. From  46  : "Migration data for the United States are based on year-to-year address changes reported on individual income tax returns filed with the IRS. " Data files are made available for download cleaned and preprocessed, in comma-separated values files (. csv file extension).
A machine learning-based approach allows the identification of HERVs associated with CTL response To optimize the epitope detection, we developed a new pipeline for annotating HERVs. For this, we reviewed multiple HERV databases and selected a recent and complete reference of 3173 HERVs, mostly composed of complete proviral sequences, thus having a higher probability of containing translated peptides  (19). We assessed HERV expression in 8893 primary tumor samples from 29 different cancer types from The Cancer Genome Atlas (TCGA) pancancer RNA sequencing (RNA-seq) database using HervQuant (fig. S1A  and table  S1 )  (15). We selected cancers with at least 10 available matched peritumoral samples (n = 14; table  S1 ) to filter HERVs highly expressed in tumors and not in normal tissue [cancer-associated HERVs (CAHs)].
Triple-negative breast cancer is characterized by many cyt-HERVs containing shared HERV epitopes Owing to the well-characterized expression of HERVs in triplenegative breast cancer (TNBC)  (24)  and the availability of an RNA-seq database comprising normal samples  (25) , we then focused on breast cancer. Differential HERV expression analysis uncovered a total of 497 CAHs expressed across different breast cancer subtypes, among which 91 were annotated as cyt-HERVs (Fig. 3, A  and B ). Fifty-four of these 91 cyt-HERVs were expressed in the basal subtype (Fig. 3B ). The mean expression of these 54 cyt-HERVs was significantly higher in TNBC and ER + (estrogen receptor-positive) samples compared to peritumoral or normal breast tissues from an independent dataset (Fig. 3C ). Analysis of the mean expression level of these 54 HERVs in medullary thymic epithelial cells (mTECs) revealed a similar profile as in normal tissues (Fig.
To gain further insight, we submitted the 3D models to binding affinity prediction (Fig. 5D ). When compared to reference TCR-pMHC complexes available in the Protein Data Bank (which were obtained from crystallography data), the predicted affinities of the identified TCRs match clinically relevant TCR affinities, such as TCRs targeting MAGE-A3, NY-ESO-1, MART-1, human T cell leukemia virus, or cytomegalovirus (CMV) (Fig. 5E ). Hence, the HERV-specific TCRs identified are predicted to stably interact with their respective pMHC complexes, reminiscent of high-affinity TCRs.
Datasets For RNA-seq data, raw fastq files were accessed from the National Center for Biotechnology Information Gene Expression Omnibus portal, under the accession number GSE58135 for Varley et al. (25)  independent breast cancer dataset, GSE74246 for the sorted PBMC dataset  (33) , and GSE127825 and GSE127826 for the six mTEC samples  (34). TCGA pancancer raw fastq files were accessed from the Genomic Data Commons portal (https://portal. gdc. cancer. gov/). Cell line data were accessed from the Broad Institute Cancer Cell Line Encyclopedia portal (https://portals. broadinstitute. org/ccle).
Immune signatures and genetic alterations Phenotypic immune signatures were calculated with the Xcell method  (38). For the TCGA pancancer samples, Xcell signatures were directly downloaded from the Xcell website (https://xcell. ucsf. edu/ xCell_TCGA_RSEM. txt). For the GSM1401648 dataset, signatures were calculated for the whole dataset, and immune signatures were filtered after. IFN- signature was calculated by single-sample gene set variation analysis  (39)  based on the HALLMARK_INTERFERON_ GAMMA_RESPONSE signature from the Molecular Signature Database (http://software. broadinstitute. org/gsea/msigdb/index. jsp). Enrichment scores were calculated for each sample per cancer type. The cytolytic activity (CYT_score) was calculated as the geometric mean of granzyme B (GZMB) and perforin (PRF1) expression, as previously described  (29).
Phenotype criteria included an association with either CD4 or CD8 + T cell signatures as defined by the Xcell method  (38). Function criteria included an association with either IFN- or the cytolytic activity, defined by the geometric mean of granzyme A (GZMA) and perforin (PRF1) expression  (29). Normal PBMC expression was assessed in an independent dataset of sorted PBMCs from healthy donors  (33). HERV expression was compared independently in T and NK cells to the rest of PBMCs.
Analysis of peptidome proteomic datasets Raw MS/MS datasets were downloaded from CPTAC  (23)  for breast cancer studies  (45, 46). Retrieved MS/MS spectra were converted to MGF format using msconvert from proteowizard  (47). Then, the list of peptides was analyzed using the standalone version of Pepquery (v. 1. 6. 2. 0)  (48). The used command line was as follows: java -Xmx10G -jar pepquery. jar -fixMod 6,62,108 -varMod 117 -maxVar 3 -c 1 -tol 10 -tolu ppm -minScore 12 -e 1 -um -hc TRUE -n 1000 -itol 0. 05 -m 1 cpu 12 -pep ${peptides_list} -db ${Reference_database} -ms ${MS_database} -o ${output_directory}. For the second dataset, the fixmod and varMod in the command line were adapted similar to the following: -fixmod 6,103,157 -varMod 101,117.
Generation and refinement of 3D models The full TCR sequences of both  and  chains were reconstructed for each T cell clone from the results of the immunosequencing as Downloaded from https://www. science. org on February 02, 2022 previously published  (51). For variable domains, TRA and TRB complementarity-determining region 3 (CDR3) nucleotide sequences were obtained from immunosequencing (Fig. 5A ) and the 5′ and 3′ ends of the TRAV and TRBV regions were obtained from the International Immunogenetics Information System online database. Human constant domains of TRA and TRB were added in 3′ of the variable domains to reconstitute the full-length TCR. These full-length TCR sequences, together with the MHC and peptide sequences, were submitted to the CBS TCRpMHCmodels-1. 0 web server, specifically developed for the automatic structural modeling of TCR-pMHC complexes (52) using template-based modeling.