dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank  Hélène Bracq-Dieye  for technical help,  Georges Michel Haustant ,  Cédric Fund ,  Elodie Turc ,  Laure Lemée ,  Biomics Platform ,  C2RT ,  Institut Pasteur, Paris, France , supported by  France Génomique  ( ANR-10-INBS-09-09 ) and IBISA for 16S sequencing,  Auguste Fourneau  and  Amandine Brunet  for PCR assays and  Sandrine Isaac ,  Henrik Salje  and  Olivier Disson  for critical reading. L. H. is supported by the  Pasteur-Paris University (PPU)   International Ph. D. Programme , funded by the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No  665807 , the ' Ecole Doctorale FIRE-Programme Bettencourt ' of the  CRI Paris  and the  Fondation Pasteur Suisse. M. L. laboratory is funded by  Institut Pasteur ,  Inserm , the  European Research Council and Laboratoire d'Excellence Integrative Biology of Emerging Infectious Diseases.
1 Lm is more prevalent in host-associated environments than non-pathogenic Listeria species. Relative abundance and prevalence of Listeria sensu stricto species in 16S rRNA gene datasets in a different environments, b in selected host datasets (i. e. farm animals and/or known Lm reservoirs which were present >100x in our datasets) for which metadata detailing the host species were available and c from different sampling sites of healthy human hosts for which detailed metadata on body sampling site were available. Numbers on the right indicate samples per category.
Finally, we have characterised the SIT uncertainty due to the snow depth from an ensemble of SIT solutions computed for the Arctic by using the different snow depth products previously used in the comparison with the ASD product. During the period of 2013-2019, we found a spatially averaged SIT mean standard deviation of 20 cm. Deviations between SIT estimations due to snow depths can reach up to 77 cm. Using the ASD data instead of W99m to estimate SIT over this time period leads to a reduction in the average SIT of about 30 cm. Published by Copernicus Publications on behalf of the European Geosciences Union. F. Garnier et al. :  Advances in altimetric snow depth estimates based on bi-frequency Ka-Ku measurements 1 Introduction Since the launch of CryoSat-2 (CS-2) in 2010  (Wingham et al.
DuST The DuST  (Lawrence et al. , 2018)   data; see Sect. 2), the DuST product uses SAR mode freeboard estimates of CS-2. The CS-2 and SARAL (LRM data) data are then calibrated against data from National Aeronautics and Space Administration's (NASA's) OIB airborne measurements to align the freeboard observations from SARAL/Altika with the snow-air interface and the freeboard observations from CS-2 with the snow-ice interface. Although this method provides a generic approach adaptable to different footprints (e. g. ICESat-1 and 2), it has the disadvantage of being dependent on the OIB data which are mainly located near the Canadian Archipelago and the Beaufort Sea and are only obtained during March and April.
CryoSat Validation EXperiment (CryoVEx) These airborne data are a joint effort of the DTU National Space Institute and ESA (in cooperation with AWI). The main goal of the project is to quantify and vali- date CS-2's ability to measure SIT. Since 2003, the Cry-oVEx missions have provided both laser (ALS, airborne laser scanner) and Ku-band radar (ASIRAS, ESA airborne Ku-band interferometric radar) altimetric measurements. In addition, in 2017, the data include for the first time along-track total freeboards from the Ka-band KAREN altimeter  (Haas et al. , 2017) , allowing us to compute Ka-Ku snow depth estimations. These data have been devel-   2015). Snow depth data are collected at 4 h intervals from acoustic sounders. In this study we analyse the data of three winters  (2013-2014, 2014-2015 and 2015-2016).
As in the Arctic, AMSR2-NSIDC annual and interannual variability is lower and much more localised than in the ASD data. ASD and AMSR2-NSIDC re- produce well the thin snow pattern of the Ross Ice Shelf. One other relevant difference between these two datasets is the systematic decrease in snow depth in October in the AMSR2-NSIDC data (see Table  A2 ). The presence of thicker snow in East Antarctica for ASD is consistent with  Worby et al. (2008b)  in that comparisons with ARISE in situ data have shown radiometric measurement snow depth underestimations. Wet-snow conditions due to flooding might be responsible for radiometric brightness temperature contrasts. The low variabilities in the Weddell Sea for ASD were not expected since winter snow properties are extremely variable  (Massom et al.
Deviations of only a few centimetres of snow depth (≈ 6 cm of mean deviation between ASD and W99m snow depths in 2015; see Fig. 1 ) have strong impacts on the freshwater budget. Although this provides a relevant overview, such largescale pan-Arctic means do not show the spatial and temporal variability in the various SIT solutions. For instance, while DuST and W99m provide comparable SIT pan-Arctic mean estimations, the spatial and temporal information included in these two datasets are very different (e. g. Figs. 2  and 3 ). To better characterise the impacts of snow depth on SIT, we propose to consider the deviations between these SIT estimates for each month of each year and at each grid point. Such an ensemble of SIT solutions will allow us to characterise the level of uncertainty due to discrepancies between the different snow depths.
Deviations between SIT estimations reached up to 77 cm (55 % of pan-Arctic mean SIT) in marginal and coastal zones. We have highlighted in this study that the DuST and ASD products, albeit computed from data acquired by the same sensors at the same time, present some significant differences due to the methodology applied. The main difference is that for ASD we compare LRM data (PLRM for CS-2), while DuST compares LRM data with SAR data. The advantage of comparing LRM data together is that they have comparable footprint sizes, by which the difference reduces the effects of surface roughness highlight the effects of volume backscatter and penetration. DuST, on the other hand, relies on the calibration of the measurements on OIB without taking into account effects of the surface roughness. In fact, differences in surface roughness and the effect it has on radar penetration and retrieval of surface elevation is currently one of the other main contributors to the SIT uncertainty budget (e.
OIB snow depth data presented in this paper are the NSIDC OIB Quicklook version (Kurtz et al. (Richter-Menge et al. , 2006; idated from in situ data of the BROMEX campaign(Nghiem et al. , 2013). These data are available at https://daacdata. apps. nsidc. org/pub/DATASETS/ICEBRIDGE/Evaluation_Products/(lastaccess:March2021). For comparison with the ASD dataset, we use all spring campaigns during the period of 2014-2018. For comparison with the ASD climatology, we use all OIB spring campaigns during 2009-2012. 3. 3. 3Icemassbalance(IMB) buoysIMB data(Perovich et al. , 2021)are issued from the US Army CRREL-Dartmouth Mass Balance Buoy Program. They provide point measurements of the ice mass balance characterised by a combination of snow and ice conditions(Richter-Menge et al. , 2006; Perovich and Richter-Menge,.
Background: We collected COVID-19 mortality data and the age distribution of the deceased in France and other European countries, as well as specifically in the cities of Paris and Marseille, and compared them. (2) Methods: Data on mortality related to COVID-19 and the associated age distribution were collected from government institutions in various European countries. In France, data were obtained from INSEE and Santé Publique France. All-cause mortality was also examined in order to study potential excess mortality using EuroMOMO. The Marseille data came from the epidemiological surveillance system. (3) Results: France is one of the European countries most impacted by COVID-19. Its proportion of deaths in people under 60 years of age is higher (6. 5%) than that of Italy (4. 6%) or Spain (4.
Governmental Policies against COVID-19 The number of SARS-CoV-2 tests per 1000 inhabitants was obtained using data from the aggregator site Our World in Data as of 2 June 2020  [10]. In order to study the severity of governments' responses to the epidemic, an overall government response Severity Index, which is a composite measure based on 9 indicators including school closures, workplace closures and travel bans, reduced to a value between 0 and 100 (100 = strictest response), was retrieved from Oxford COVID-19 Government Response Tracker and Blavatnik School of Government  [11]. Information on lockdown policy was also collected.
COVID-Related Mortality in France and Other European Countries The mortality associated with COVID-19 in France and different European countries during the first wave of the COVID-19 spread was collected from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University  [1]  between January 2020 and 2 June 2020. To correct the biases linked to the size of the countries, we calculated the mortality rate per million inhabitants. The case fatality rate (CFR, the ratio of the number of deaths to the number of confirmed cases, i. e. , the lethality rate) was obtained from the aggregator site Our World in Data as of 2 June 2020. The age distribution of individuals who died from COVID-19, when available, was collected for several countries. Data for the United Kingdom were collected from the National Health Service (NHS) (https: //www.
Excess All-Cause Mortality in France and Other European Countries All-cause mortality in France and at the regional level according to the decedents' place of residence between 1 January and 31 August for 3 years (2018, 2019 and 2020) was collected from the INSEE database  [15]  to assess potential excess mortality. Excess mortality in Paris and Marseille was also studied using the same database. The excess mortality by country and by age group (0-14, 15-44 and 45-65 years) was retrieved from the EuroMOMO website (https://www. euromomo. eu/graphs-and-maps, accessed on 11 June 2021), which collects all-cause mortality data from several European partner countries such as France, the United Kingdom, Italy and Spain. The excess mortality was estimated using the z-score, which allows comparisons of mortality between the different countries and the different time periods studied  [16].
French Departmental and Regional Data on COVID-19 Daily hospital data related to COVID-19 by French region and department were obtained from Santé Publique France, a public health institute in France  [17]. These data were available only from 19 March onward, and our study period was therefore limited to 19 March to 2 June 2020. To correct for biases related to population size in each region and department, we calculated the mortality rate in France per million inhabitants. The size of each regional population was retrieved from the Institut National d'Études Démographiques (INED) database  [18]. The population of each department was collected from the INSEE database. Deaths associated with COVID-19 by age group according to region were retrieved from GEODES, the French Public Health mapping observatory, over the same study period [19].
Marseille Data Local data for the city of Marseille were obtained by using the epidemiological surveillance system from our institute that collects information on patients hospitalized at Assistance Publique-Hôpitaux de Marseille (AP-HM), which comprises four public university hospitals  [22, 23]. This system is based on the results from the clinical microbiology laboratory of the IHU Méditerranée Infection, and includes microbiological results (sample type, sample date, requesting unit) and anonymous patient information (age, sex, home postal code, date of admission), stored in a data warehouse using MariaDB. COVID-19associated mortality data were obtained from the Department of Medical Information (DMI) of AP-HM. The size of the Marseille population was also obtained from the INSEE database  [24].
Statistical Analysis The statistical analyses were performed with OpenEpi software (https://www. openepi. com/TwobyTwo/TwobyTwo. htm?fbclid=IwAR0NjbfgL6G7d77LiFSYTzdJAbK3YIPaYi2ZD FEeCnhFqbHFuMfibs1jaWI) (accessed on 11 June 2021). A chi-square test or mid-P test was used to compare groups, depending on the variables. The graphs were created using the software R  [25]  with the ggplot2 package  [26]  and using Excel.
Strain Sequencing and Integration Site Identification The complete genomes of JMY8032 and JMY8208 strains were fully sequenced by Eurofins Genomics (Ebersberg, Germany) using the Illumina Novaseq 6000 platform (paired-end 2 × 150 bp). For DNA extraction, cells were grown at 28 • C in 10 mL of YPD medium for 24 h and harvested by centrifugation. Total genomic DNA was then extracted from cell pellets using the MasterPureTM yeast DNA purification KIT from Epicentre (Madison, WI, USA) according to the manufacturer's instructions. The resulting DNA pellets were dissolved in 100 µL TE (Tris-HCl 10 mM, EDTA 1 mM, pH 8. 0) with 1 µL RNase A (10 mg/mL), incubated at 37 • C for 15 min, and ethanol precipitated. Finally, the DNA was dissolved in Tris buffer (10 mM, pH 8). Raw sequencing data were deposited to the SRA database and are available under the Project accession number PRJNA715500 (data accession number SRR14075915 for strain Y8032 and SRR13999501 for strain Y8208).
As shown in Figures  1D  and S2A , all these experiments indicated that the sets of STAG2-modulated genes defined with isogenic clones were regulated as soon as 24-48 h after siRNA transfection. At 72 h, most of the gene expression changes detected in the isogenic clones were observed, with only minor variations between siRNAs or cell lines and with a lower dynamic range of modulation than in stable KO experiments (Figure  S2A ). We can therefore conclude that this set of genes represents a robust signature of STAG2 KO Ewing cells, modulated at short term and which hence accounts for early, possibly direct, transcriptomic consequences of STAG2 inactivation. Functional analysis reveals major impact of STAG2 inactivation on EWSR1-FLI1-induced genes The functional aspect of STAG2-proficient and -deficient Ewing sarcoma was investigated by Gene Set Enrichment Analysis (GSEA) and the DoRothEA-curated transcription factor/target gene set database  (Garcia-Alonso et al.
, 2019; Mootha et al. , 2003). Taking advantage of additional Ewing sarcoma cell line and tumor RNA-seq data, we performed this analysis in four independent Ewing sarcoma datasets: (1) STAG2 WT parental cells versus STAG2 KO isogenic cells, (2) STAG2 WT si-control versus STAG2 KD transfected cells, (3) STAG2 WT versus STAG2 mutated Ewing cell lines, and (4) STAG2 WT versus STAG2 mutated Ewing tumors. Strikingly, when investigating 18,889 signatures ranked by average normalized enrichment score (NES), several of the top 20 signatures enriched in STAG2 proficient condition were EWSR1-FLI1-regulated gene signatures (Tables  1  and S2 ). Ranking first in GSEA and DoRo-thEA analyses (Tables  1  and S3 ), IC-EWS is a recently described signature  (Aynaud et al. , 2020)  that was defined based on independent component analysis of single-cell RNA-seq experiments upon induction of EWSR1-FLI1 in Ewing cells.
This signature is exquisitely specific for Ewing sarcoma, enriched in genes modulated by EWSR1-FLI1 activity on GGAA microsatellite sequences and mostly devoid of cell-cycle genes, which are frequently confounding factors in such GSEA analyses. Apart from a borderline significant signature observed at 24 h post transfection in A673 cells knocked down for STAG2, all other comparisons yielded strongly significant GSEA results with the IC-EWS gene set (Figures  S2B-S2G ). Direct comparison also showed that IC-EWS distinguish STAG2 WT and mutated cell lines and tumors  (Figures S2H and S2I). Further evidences for increased activity of EWSR1-FLI1 in STAG2-proficient conditions are provided by Riggi and Miyagawa datasets highlighting genes that are upregulated upon ectopic expression of EWSR1-FLI1 in mesenchymal/progenitor cells.
Beyond EWSR1-FLI1related signatures, other gene sets provided much less consistent information across datasets and hence appeared less meaningful (Table  1 ). One gene set each of epidermal growth factor (EGF) or transforming growth factor b (TGF-b) signaling, and one gene set each of MYC, P53, and nuclear factor kB (NF-kB) targets were ranked among the first 20 gene sets (Tables  1  and S3 ). It is noteworthy that the P53 gene set is particularly enriched in analyses performed in cell lines and in tumors, an observation that may be linked to the frequent association of STAG2 and TP53 mutations in Ewing cell lines and tumors  (Tirode et al. , 2014). In contrast to downregulated genes in STAG2 LOF systems, analyses of upregulated gene sets did not provide obvious illuminating information (Tables  1  and S3 ).
1 1. 5 1. 9 2. 4 1. 72 C C C C C ACOSTA_PROLIFERATION_ INDEPENDENT_MYC_TARGETS_DN 1. 7 1. 5 1. 8 1. 8 1. 71 C C C C C C C C AMIT_DELAYED_EARLY_GENES 1. 4 1. 4 1. 8 2. 2 1. 69 C C C C FISCHER_DIRECT_P53_TARGETS_ META_ANALYSIS 1. 3 1. 6 1. 8 2. 0 1. 69 C C C C C C REACTOME_FCERI_MEDIATED_ CA_2_MOBILIZATION 1. 3 1. 7 1. 2 2. 3 1. 64 C C C C C MIYAGAWA_TARGETS_OF_ EWSR1_ETS_FUSIONS_UP 1. 8 1. 4 1. 5 1. 9 1. 63 C C C C C C HUANG_DASATINIB_RESISTANCE_UP 1. 7 1. 6 1. 7 1. 6 1. 63 C C C C C C C C KAUER_EWS-FLI_DOWN 1. 9 1. 6 1. 5 1. 5 1. 63 C C C C C C C PLASARI_TGFB1_TARGETS_1HR_UP 1. 6 1. 5 1. 7 1. 7 1. 63 C C C C C C GSE21546_UNSTIM_VS_ANTI_CD3_ STIM_ELK1_KO_DP_THYMOCYTES_UP 1. 1 1. 2 2. 0 2. 2 1. 62 C C C C SMIRNOV_RESPONSE_TO_IR_2HR_UP 1. 4 1. 8 1. 5 1. 8 1. 61 C C C C STAG2 deficient CHR1Q44 À1.
, 2019). Interestingly, applying our Tweed algorithm to recently published CTCF HiChIP data (GSE111537) in STAG2 WT and KO acute myeloid leukaemia cell models  (Smith et al. , 2020 ) also showed a significant decrease of stripe pattern in STAG2 LOF cells (p < 2. 2 3 10 À16 ). This thus indicates that altered chromatin extrusion upon STAG2 LOF is not limited to Ewing sarcoma and is also observed in other malignancies. The folding of DNA into chromatin loops is a critical determinant of the function of the genome and particularly of gene regulation. Vian et al. (2018)  recently reported that 79% of stripe domains were associated with active enhancers. We confirm this observation and further show that EWSR1-FLI1-bound microsatellites, which act as neo-enhancers in Ewing sarcoma, are also strongly enriched in such domains.
See also Figure  S8 , Videos S1 and S2. cells with a low activity, yet keeping the appropriate window of activity for proliferation. STAG2 LOF Ewing cells might thus combine aggressive features of EWSR1-FLI1 Àhigh and EWSR1-FLI1 Àlow cells. Although our data highlight a critical role of STAG2 LOF in moderating EWSR1-FLI1 activity, it is likely that this alteration may also contribute additional oncogenic functions. In that respect, it is noteworthy that a YAP/TAZ gene set significantly shows up in STAG2 LOF upregulated genes, suggesting that YAP/TAZ activity may also contribute to lower EWSR1-FLI1 activity. DKK2 and other WNT antagonists (KREMEN1, FRZB) and agonists (LRP5, FZD8) are, respectively, down-and upregulated upon STAG2 LOF in most datasets, indicating that Wnt signaling activation may also possibly contribute to cell migration.
RNA-seq RNA was extracted from independent experiments using RNeasy Plus Mini Kit (Qiagen, ref 74134) according to manufacturer recommendations. RNA sequencing libraries were prepared from 1 mg of total RNA using the TruSeq Stranded mRNA Library preparation kit  (Illumina, ref 20020594)  following manufacturer instructions. Sequencing was carried out using 2x100 cycles (paired-end reads 100 nucleotides) for all samples on Illumina HiSeq2500 or NovaSeq6000 instruments. Reads were aligned with STAR 2. 5. 3  (Dobin et al. , 2013)  to the human genome (GRCh37/hg19 version). We used the count matrix generated by STAR using the human gene annotation v19 of GENCODE. DESEQ2 1. 20. 0 (Love et al. , 2014)  was used to normalize data and performed differential analysis with the Wald test. The p-value was adjusted using Benjamini-Hochberg.
For differential analysis, we used an adjusted p-value < 0. 01 and |log2(fold change)| > 1. We considered a gene expressed if the normalized expression is higher than 10. Tumors and cell lines expression data RNA-seq dataset from Ewing sarcoma patients were previously published:EGAS00001003333. Microarray data were profiled using HG-U133-Plus2 arrays (Affymetrix) and processed as previously published  (Postel-Vinay et al. , 2012; Surdez et al. , 2012). For Microarray analyses, Ewing tumors samples from: GSE34620 and from this study: GSE133228, were used. RNA-seq of this study: GSE133228, were generated from previously described Ewing cell lines  (Batra et al. , 2004; Dauphinot et al. , 2001; Kovar et al. , 1997; Tirode et al. , 2014). STAG2 mutational status of tumors  (Tirode et al. , 2014)  and cell lines  (Brohl et al.
This inflection point can be detected by a negative value of the second derivative of the function defined by the cumulative curve of interaction counts along the stripe. If the value was negative on the corner, or lower than -10 in the +/-1 adjacent bin to the corner, a candidate loop was predicted. Only loops predicted by both methods and with mean intensity higher than 2 reads/bin were kept. For adjacent loops (i. e. differing only by 1 bin at their CTCF boundary sites), the loop with the highest coverage intensity was kept. At this point, most predicted loops presented at their boundaries a strong prevalence of cohesin members and convergent CTCF motif orientations as determined with the FIMO tool from MEME suite  (Grant et al. , 2011)  using the CTCF motif (MA0139. 1 from Jaspar database) (Figures  S5E-S5G ).
Image analysis To analyze the shape and size of cellular aggregates, raw images are binarized through a custom-made MATLAB code. Briefly, the code detects the contours of the aggregate with a standard-deviation filter. Contour are then filled to define the shape of the aggregate, whose geometric properties are assessed through MATLAB's regionprops function. In particular, we record for each frame the area and the eccentricity, defined as the ratio of the distance between the two foci of the equivalent ellipse and the length of the long axis. This value is equal to 1 for a straight line and 0 for a perfect circle.
Velocity measurements: Particle image velocimetry (PIV) was used to obtain the velocity fields around the contour of the aggregates. The PIV analysis was performed using the Matpiv package in MATLAB (https://www. mn. uio. no/math/english/people/aca/jks/matpiv/). A three-pass computation using a final window of 64 x 64 pixels (195 x 195 mm) was used, with a. 75 overlap. Aberrant vectors were detected and removed from the analysis when their magnitude exceeded the local median value by three times the standard deviation. The time interval between consecutive images was 4 hours. For clarity, we only draw the velocity arrows located at less than 100 mm from the edge of the aggregate. Boyden chambers assay A673 and CHLA-258 cells were transfected 48hrs prior the assay. The following numbers of trypsinized cells were seeded in the upper compartment of a Boyden chamber (353182, BD Biosciences): 5X104 A673, 5X104 A673SA2m#1, 5x104 A673SA2r cells and 7.