dataesr/scientific-paragraphs-categorization huggingface dataset:
4C-seq 4C-seq experiments performed in synchronized cells, before and after DSB induction, were performed as described previously  7. In brief, 10-15 × 10 6 DIvA cells per condition were cross-linked, lysed and digested with MboI (New England Biolabs). DNA ligation was performed using the T4 DNA ligase (HC) (Promega), and ligated DNA was digested again using NlaIII (New England Biolabs). Digested DNA was religated with the T4 DNA ligase (HC) (Promega) before proceeding to 4C-seq library preparation. A total of 16 individual PCR reactions was performed to amplify around 800 ng of 4C-seq template, using inverse primers including the Illumina adaptor sequences and a unique index for each condition (Extended Data Table  2 ). Libraries were pooled and sent to a NextSeq 500 platform at the I2BC Next Generation Sequencing Core Facility (Gif-sur-Yvette).
4C-seq data were processed as described previously  7. In brief, BWA-MEM was used for mapping and Samtools was used for sorting and indexing. A custom R script (https://github. com/bbcf/bbcfutils/ blob/master/R/smoothData. R) was used to build the coverage file in bedGraph format, to normalize using the average coverage and to exclude the nearest region from each viewpoint. Differential 4C-seq data were computed using BamCompare from deeptools with bin-size=50 bp. The average of total trans interactions between viewpoints and DSBs was then computed using a 1 Mb window around the breaks (80 best) and after exclusion of viewpoint-viewpoint (cis) interactions.
Hi-C Hi-C data obtained before and after DSB induction and after control or SCC1 depletion in DIvA cells were retrieved from a previous study  7. Hi-C experiments with or without DSB induction and after ATM or DNA-PK inhibition, or after transfection with control or SETX siRNAs were performed in DIvA cells as described previously  7. In brief, 10 6 cells were used per condition. Hi-C libraries were generated using the Arima Hi-C kit (Arima Genomics) according to the manufacturer's instructions. DNA was sheared to an average fragment size of 350-400 bp using the Covaris S220 system and sequencing libraries were prepared on beads using the NEB Next Ultra II DNA Library Prep Kit for Illumina and NEBNext Multiplex Oligos for Illumina (New England Biolabs) according to the instructions of the Arima Hi-C kit.
Hi-C data analyses Hi-C heat maps. Hi-C reads were mapped to the hg19 genome and processed using Juicer with the default settings (https://github. com/ aidenlab/juicer). Hi-C count matrices were generated using Juicer at multiple resolutions: 100 kb, 50 kb, 25 kb, 10 kb and 5 kb. Hi-C heat map screenshots were generated using Juicebox (https://github. com/aidenlab/Juicebox/wiki/Download). Aggregate heat maps were computed on a set of submatrices extracted from the originally observed Hi-C matrices at 50 kb resolution or 100 kb resolution. The region of 5 Mb around DSBs (80 best) was extracted and then averaged. The log 2 -transformed ratio was then computed using Hi-C counts (+DSB/-DSB) and plotted as heat maps. Cis contact quantification. For cis contact quantification, interactions within γH2AX domains (-0.
01, giving 1,206 narrow peaks. Of these peaks, only interchromosomal pairs (trans) were retained using a BEDPE format file. Random positions were generated using the gkmsvm package and pairs of interactions were built according to the same procedure. APA was then performed on these trans pairs using juicertools. TAD cliques. TAD cliques were computed using the igraph R package on an undirected graph representing DSB clustering. This graph was computed on the differential Hi-C matrix (+DSB/-DSB) counts, at 500 kb resolution, considering a change of around 86% of interaction (0. 9 in log 2 ) between two DSBs as a node on the graph. Averaged ChIP-seq signal values (53BP1/γH2AX/H1/Ubiquitin FK2) were then computed for each category of cliques using 500 kb windows around DSBs. For prior RNA polymerase II occupancy, the signal was computed on 10 kb around DSBs.
3, unexpressed genes filtered), giving four categories. Without DNA-PKi, upregulated/ compartment D (n = 40); upregulated/no compartment D (n = 32); downregulated and not regulated/compartment D (n = 453); downregulated and not regulated/no compartment D (n = 314). Transcription factor motif analysis. Transcription-factor-binding motifs were extracted on the promoter regions (-500 bp of the transcription start site (TSS)) of genes with positive value of D compartment (2,161) versus genes with negative value (2,112) identified on chromosomes 1, 2, 6, 9, 13, 17, 18, 20 and X from the PCA analysis of Hi-C (+DSB + DNA-PKi/-DSB) using motifmatchr and TFBSTools R packages on the JASPAR2020 database. Motifs were sorted by significance using Fisher's exact test and adjusted using the Benjamini-Hochberg procedure between motifs found on gene inside the D compartment versus genes outside D compartment.
Correlation with DRIP-seq or qDRIP datasets. To assess a correlation between R-loop accrual and D-compartment formation, R-loop levels obtained from DRIP-seq experiments performed in DIvA cells  38  were computed on extended gene bodies (±2 kb TTS) and plotted as a box plot for categories (Figs. 3f  and 4c ). Conversely, to establish whether R-loop enriched genes, display higher levels of differential CEV (D compartment) signal (Extended Data Fig. 6c ), R-loop levels were computed on all genes of chromosome 1, 17 and X (±1 kb of the TTS) and further categorized into 10 groups (based on percentiles). The D-compartment signal was compared between the highest (n = 180) and lowest (n = 190) groups. For qDRIP experiments, given that the signal accumulates on narrow peaks within genes (in contrast to DRIP-seq), we used the identified locations of narrow peaks (see the 'qDRIP-seq' section) inside the genes in each category (±2 kb of the TTS) (Fig.
The significance of the overlap between genes and breakpoints was determined using the regioneR package  56  by using resampling test with PermTest. In brief, we selected 1,000 times a control set of genes, with the same size and on the same chromosome as our original gene set. We tested the overlap between each gene and breakpoints to determine a distribution of the number of overlaps between control set and breakpoints. We further tested whether the overlap between our gene set (D compartment or non-D compartment) and breakpoints was significant by counting the number of times more overlaps occurred in the control set than in our gene set. GenomicRanges, plyranges, tidyverse, patchwork, ggforce, ggside and ggtext were used to read, manipulate and visualize genomic data in R and produce figures.
Acknowledgements We thank the members of the  Genomics Core Facility of EMBL  and the  High-Throughput Sequencing Facility of I2BC  for high-throughput sequencing as well as the staff at the  Light Microscopy (LITC) Facility  at the  CBI ;  S. Britton  and  P. Calsou  for the pICE-RNaseHI-WT-NLS-mCherry plasmid; and  L. Ligat  from the  CRCT Cell Imaging Platform (INSERM- UMR1037 ) for technical assistance in high-content microscopy. RNAscope data for this study were acquired at the  Center for Advanced Light Microscopy-Nikon Imaging Center at UCSF  on the W1-CSU confocal system obtained using a  NIH   S10 Shared Instrumentation grant  ( 1S10OD017993-01A1 ). M. B. and A. S. B. were supported by  Cancer Research UK  core funding to the  Beatson Institute  ( A31287 ) and to the  Bushell laboratory  ( A29252 ); A.
S. B. by the  National Productivity Award  from the  MRC  ( MC_ST_U17040 ). Funding in the G. L. laboratory was provided by grants from the  European Research Council  ( ERC-2014-CoG 647344  and  ERC-AdG-101019963 ), the  Agence Nationale pour la Recherche  ( ANR-18-CE12-0015 ), the  Association Contre le Cancer (ARC) , the  ITMO Cancer (RiDR) , the association  Robert Debré  and the  Fondation Bettencourt-Schueller. C. A. was a recipient of a  FRM fellowship  ( FRM FDT201904007941 ) and is a recipient of  EMBO postdoctoral fellowship  ( EMBO ALTF 585-2021 ). Funding in the F. E. laboratory was provided by a grant from the  European Research Council  ( ERC-2018-StG 804023 ). F. S. is a recipient of a  LNCC fellowship. S. C. is a recipient of a PhD fellowship from the  Joint Training and Research Programme on Chromatin Dynamics & the DNA Damage Response (H2020 ITN aDDRess , grant no.
Data availability All high-throughput sequencing data (Hi-C, 4C-seq, amplicon-seq, qDRIP-seq and RNA-seq) performed in this study have been deposited at Array Express under accession number E-MTAB-10865. Other high-throughput sequencing data used in this study are available under accession numbers E-MTAB-8851 (Hi-C data before and after DSB induction and after control or SCC1 depletion; pATM ChIP-seq); E-MTAB-5817 (BLESS before and after DSB induction; LIG4, 53BP1, γH2AX, FK2, histone H1 and H3K79me2 ChIP-seq experiments); and E-MTAB-6318 (DRIP-seq before and after DSB induction; SETX ChIP-seq). Breakpoint positions of interchromosomal translocations across 18 different cancer types were retrieved from ref. 42.
Intravascular ultrasound analysis IVUS images were recorded as reported previously  [2]. Imaging analysis was performed with Echoplaque version 3. 0. 28 (Indec Medical Systems, Santa Clara, CA, USA). Paired IVUS recordings from a single center were analyzed by an independent core laboratory (CERC-European Cardiovascular Research Center, Massy, France) by blinded assessors using CAAS IntraVascular 2. 0: Pie Medical Imaging B. V. software. The analyses were performed by experts qualified based on inter-and intra-observer variability testing and all data were reviewed by a second medical expert prior to approval. Vasomotion was assessed pre and post intracoronary NTG (200 μg) injections. For the pulsatility analysis  [7] , stationary loops were captured over a minimum of 3 cardiac cycles to identify minimal and maximal lumen areas corresponding to systolic and diastolic phases, respectively.
Endpoints and definitions The full list of endpoints has been published previously  [2] , and is available on ClinicalTrials. gov (NCT03429894). Clinical endpoints at 24 months are target lesion failure (TLF), a composite of cardiac death  [8] , target-vessel myocardial infarction (TV-MI), and clinically-driven target lesion revascularisation (CD-TLR); target vessel failure, a composite of cardiac death, TV-MI and clinicallydriven target vessel revascularisation (CD-TVR); MI; TLR; TVR; and definite or probable device thrombosis  [8]. Myocardial infarction was defined as enzyme elevation of 2-times upper normal limit of CK with elevation of CK-MB. IVUS endpoints have been reported previously  [2] , with the exception of the endpoint vasomotion/ pulsatility, that -due to the complexity of the assessments and additional training requirements and qualification -could only be assessed at one center.
Statistical analysis The analysis is based on the intention-to-treat population. Continuous variables are expressed as mean ± standard deviations, categorical variables are expressed as frequencies. All calculations are based on the available data. The one-sided t-test was used to compare paired or unpaired data sets with P ≤ 0. 05 considered significant. The analysis of IVUS data was performed by an independent statistician at CERC using SAS version 9. 3 (SAS Institute, Cary, North Carolina) and clinical data were analyzed using Excel® for Microsoft 365 MSO (Version 2109 Build 16. 0. 14430. 20292) and Graphpad Prism 9 (GraphPad Software, San Diego, CA).
Further work is required to confirm the initial findings from this report. The currently enrolling INFINITY SWEDEHEART registry-based randomized controlled trial and the BIOADAPTOR RCT (NCT04562805, NCT 04192747) will be able to directly compare the DynamX Bioadaptor versus contemporary DES. Additionally, long-term followup is needed to see if the use of the DynamX Bioadaptor is able to level the non-plateauing increase in late events seen with conventional DES.
Introduction The emission of Solar Induced Fluorescence (SIF) by the pigment chlorophyll in terrestrial plants has been used as a probe for photosynthetic activity at local to global scale  (Mohammed et al. , 2019). Over the last ten years, the availability of remote sensing based SIF data has increased along with the number of hyperspectral instruments in orbit suitable for the retrieval of SIF: TANSO (GOSAT), SCIAMACHY (ENVI-SAT), GOME-2 (MetOp), CarbonSpec (TanSat), TROPOMI (Sentinel 5-P), and OCO-2. Three other missions, the dedicated Fluorescence Explorer (FLEX) mission and the geostationairy missions Sentinel-4 and TEMPO, are expected to provide SIF data in the coming years. With the growing interest in the use of the signal of SIF in vegetation remote sensing the number of flux tower sites equipped with spec-trometers for the monitoring of SIF in the field has increased too  (Marrs et al.
MODTRAN5 simulations In order to test the validity of the assumptions of the approach, we used simulations with the atmospheric radiative transfer model MOD-TRAN5. To ensure realistic input data, we used atmospheric properties obtained from Aeronet sites during clear sky days in Italy, Germany and Spain, at Sentinel-3B overpass times (see github. com/christiaanvandertol/BSF for the input data files) and for one site (Spain), we repeated the simulations for solar zenith angles of 30, 45, 60 and 75 degrees. Simulations with different values of visibility, column water vapour, solar inclination angle, elevation and surface temperature enabled testing of the effects of extrapolation of k and convolution with the SRF. For four input data files, the surface irradiance was calculated with the T-18 system described by  Cogliati et al.
(2015)  and  Verhoef et al. (2018). The simulations were repeated after perturbation of the surface elevation in each of the four MODTRAN5 input data sets, yielding irradiance spectra at a different elevation. This resulted in four pairs of irradiance spectra, each consisting of spectra at two elevations, notably 7 and 81 m, 258 and 324 m, 111 and 125 m, and 69 and 111 m. The effect of the convolution of with the SRF was investigated and quantified with MODTRAN5 and SCOPE simulated spectra as follows. First, a synthetic 'true' upwelling radiance L in absence of fluorescence was estimated for values of a ranging from 1. 0 to 1. 1 as: L = exp ( log ( E M5 ⋅r ) ⋅a ) *S f (11) in which E M5 are the high resolution MODTRAN5 direct solar irradiance spectra normalized to the oxygen band shoulders, and r a surface reflectance for green vegetation simulated with SCOPE version 2.
The C SRF was calculated by linear regression between (L -L approx )/cos(θ s ) and (a -1) for each wavelength of the instrument (FLoX) in the O 2 bands (see github. com/christiaanvandertol/BSF for the source code). The SRF for the FWHM of 0. 31 of the FLoX system (C SRF,FWHM=0. 31 ), was archived and further used in the BSF algorithm to correct for the infilling of L due the spectral response function as follows: L c = L + (a -1)⋅C SRF cos(θ s ) (13) The effect of the extrapolation of k was investigated by carrying out linear regression on each of the four pairs (of elevations) of MODTRAN5 simulations. The resulting slope of the regression, which resembles a, was compared to the equivalent estimated from the barometric equation (Eq. (  17 )). The residual of the regression provides insight into spectral differences in k in the portion of the atmosphere for which k was extrapolated.
Finally, the atmospheric transmittance T 2 of F over the optical path x 2 was estimated from a by using a simplified representation of the illumination-observation geometry. Following the definition sketch in Fig. 1  and neglecting diffuse irradiance by first approximation, x 2 can be expressed as a function of x 1 and the solar zenith angle θ s and observation viewing angle θ o as shown in Fig. 1 : x 2 = x 1 cosθ s cosθ o (14) By substitution of x 0 = logE/E a , x 2 = logT2 -k and x 1 = x 2 cosθo cosθs in a = (x 0 +x 1 +x 2 )/ x 0 , T 2 can be expressed as: logT 2 = (a -1)log E Eo 1 + cos(θo) cos(θs ) (15) With these parameters set, the simultaneous retrieval of a and F from E and L is carried out in a number of steps to minimise the cost function as in pseudo-code below (Algorithm 1). The following cost function was minimized using a non-linear least square solver, where each update is defined with a Trust Region algorithm: e = log πL r L o -F -alog E E o (16) We used the built-in function 'lsqnonlin' in Matlab 2017a with stopping criteria described in Table  1.
Our results highlight emissions from gas power plants in the northeast of the country and from the urban area of the capital, Doha. The emissions from cement plants in the west and different industrial facilities in the southeast are underestimated due to frequent low-quality measurements of NO 2 columns in these areas. Our top-down model estimates a weekly cycle, with lower emissions on Fridays compared to the rest of the week, which is consistent with social norms in the country, and an annual cycle, with mean emissions of 9. 56 kt per month for the 4-year period. These monthly emissions differ from the Copernicus Atmospheric Monitoring Service global anthropogenic emissions (CAMS-GLOB-ANT_v5. 3) and the Emissions Database for Global Atmospheric Research (EDGARv6. 1) global inventories, for which the annual cycle is less marked and the average emissions are respectively 1.
This article is organized as follows: Sect. 2 provides a description of Qatar and its main emission sources. Section 3 provides a description of the datasets used in this study. Section 4 presents the flux-divergence method to infer NO x emissions at the scale of the country. Section 5 presents the spatial distribution of NO x emissions and their seasonal variations. It also presents the limitation of our method in terms of inferring emissions for areas above which TROPOMI measurements are often of low quality. Section 6 compares our estimated emissions to local electricity generation and existing global inventories to provide a sectoral approach to NO x emissions. Section 7 presents the limits and the uncertainties of the model, and Sect. 8 presents our concluding remarks. For the purposes of our study, NO x emissions are expressed as NO 2 throughout this article.
Instruments and data 3. 1 TROPOMI NO 2 retrievals NO 2 can be observed from space with satellite instruments based on its strong absorption features in the 400-465 nm wavelength region  (Vandaele et al. , 1998). By comparing observed spectra with a reference spectrum, the amount of NO 2 in a portion of the atmosphere between the instrument and the surface can be derived. The TROPOspheric Monitoring Instrument (TROPOMI), on board the European Space Agency's (ESA) Sentinel-5 Precursor (S-5P) satellite, is one of those instruments, providing daily measurements of NO 2 around 13:30 local time (LT; the time zone for all instances in the text is LT). Its high spatial resolution (originally 3. 5×7 km 2 at nadir, improved to 3. 5×5. 5 km 2 since 6 August 2019) allows one to observe some of the fine-scale structures of NO 2 pollution, such as within cities  (Beirle et al.
The large swath width of the instrument (∼ 2600 km) makes it possible to construct NO 2 images of VCDs on large spatial scales. We use TROPOMI NO 2 retrievals from 2019 to 2022 (S5P-PAL reprocessed data with processor version 2. 3. 1 from January 2019 to October 2021, offline (OFFL) stream with processor version 2. 3. 1 from November 2021 to October 2022 and OFFL stream with processor version 2. 4. 0 from November to December 2022) over Qatar. The arid climate of the country, which offers a large number of clear-sky days throughout the year, enables the calculation of monthly averages based on multiple observations. Its intensive anthropogenic activity, concentrated on a small number of areas, allows one to observe high NO 2 concentration patterns. Due to its short lifetime (about 1 to 10 h), background NO 2 levels can be orders of magnitude lower than levels near polluted areas.
CAMS real-time fields The Copernicus Atmospheric Monitoring Service (CAMS) global near-real-time service provides analyses and forecasts for reactive gases, greenhouse gases and aerosols. Data are gridded on 25 vertical pressure levels with a horizontal resolution of 0. 4 • × 0. 4 • and a temporal resolution of 3 h  (Huijnen et al. , 2019). Here, CAMS concentration fields of hydroxyl radical (OH) are used to calculate NO 2 sinks from TROPOMI observations. We also use CAMS temperature field T to account for the reaction rate for HNO 3 production through the calculation of the NO 2 lifetime according to  Burkholder et al. (2020). The hourly values are also linearly interpolated to the TROPOMI orbit timestamp and regridded to a 0. 0625 • × 0. 0625 • resolution.
Inventories The Emissions Database for Global Atmospheric Research (EDGARv6. 1) and the CAMS global anthropogenic emissions (CAMS-GLOB-ANT_v5. 3) are global inventories that provide 0. 1 • × 0. 1 • gridded emissions for different sectors on a monthly basis. EDGARv6. 1 emissions are based on activity data (population, energy production, fossil fuel extraction, industrial processes, agricultural statistics, etc. ) derived from the International Energy Agency (IEA) and the Food and Agriculture Organization (FAO), corresponding emission factors, national and regional information on technology mix data, and end-of-pipe measurements. The inventory covers the years 1970-2018. CAMS-GLOB-ANT_v5. 3 is developed within the framework of the Copernicus Atmospheric Monitoring Service  (Granier et al. , 2019).
Availability of TROPOMI data above industrial sites High-quality measurements by TROPOMI are infrequent above the western and southeastern parts of Qatar, with pixels regularly corresponding to measurements with q a < 0. 75. For these pixels, the inferred emissions are only calculated with an average over a very small number of measurements. In many cases, no measurements are available, and emissions cannot be attributed to the pixels. Figure  7  shows the mean TROPOMI coverage over the domain for the year 2020, counted as fraction of days with q a < 0. 75 within the year. In addition to the west and southeast (corresponding to the southern part of Doha) already identified, the northeast is also affected but to a lesser extent. This situation is the same for the years 2019 and 2021, and the identified areas have a fraction of observable days lower than 40 %.
Nevertheless, it allows us to evaluate the effect of non-detection of cement plants and power plants in the southeast of the domain. The use of a threshold value of q a = 0. 7 increases the inferred fluxes by 43. 8 % above cement plants in the west and by 33. 4 % above the industrial area in the southeast. Fluxes for the Ras Laffan area in the north and central Doha, which are less affected by the effect described here, are increased by 2. 6 % and 3. 0 % respectively. It is difficult to determine whether these increases are realistic because they were obtained using columns where NO 2 might have been estimated above aerosol and cloud layers. However, they indicate that our model, run with TROPOMI data before November 2021, leads to a systematic underestimation of NO x emissions in two of the four most NO x -intensive areas of the country.